{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HK074FYkNwTi"
   },
   "source": [
    "# Deep Learning with PyTorch Step-by-Step: A Beginner's Guide"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "edtdvwWONwTl"
   },
   "source": [
    "# Chapter 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a8kZ1PM4NwTm",
    "outputId": "50b86efc-2663-4a17-905e-c85e6af9ec66"
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    import google.colab\n",
    "    import requests\n",
    "    url = 'https://raw.githubusercontent.com/dvgodoy/PyTorchStepByStep/master/config.py'\n",
    "    r = requests.get(url, allow_redirects=True)\n",
    "    open('config.py', 'wb').write(r.content)    \n",
    "except ModuleNotFoundError:\n",
    "    pass\n",
    "\n",
    "from config import *\n",
    "config_chapter10()\n",
    "# This is needed to render the plots in this chapter\n",
    "from plots.chapter8 import *\n",
    "from plots.chapter9 import *\n",
    "from plots.chapter10 import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "JyxowqR5NwTp"
   },
   "outputs": [],
   "source": [
    "import copy\n",
    "import numpy as np\n",
    "\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader, Dataset, random_split, TensorDataset\n",
    "from torchvision.transforms.v2 import Compose, Normalize, Pad\n",
    "\n",
    "from data_generation.square_sequences import generate_sequences\n",
    "from data_generation.image_classification import generate_dataset\n",
    "from helpers import index_splitter, make_balanced_sampler\n",
    "from stepbystep.v4 import StepByStep\n",
    "# These are the classes we built in Chapter 9\n",
    "from seq2seq import PositionalEncoding, subsequent_mask, EncoderDecoderSelfAttn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P7lEypJINwTq"
   },
   "source": [
    "# Transform and Roll Out!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vZ5tuOx2NwTr"
   },
   "source": [
    "## Narrow Attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fBiNshjSNwTs"
   },
   "source": [
    "### Chunking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SdxrUQNTNwTt"
   },
   "source": [
    "![](https://github.com/dvgodoy/PyTorchStepByStep/blob/master/images/attn_narrow_transf.png?raw=1)\n",
    "\n",
    "![](https://github.com/dvgodoy/PyTorchStepByStep/blob/master/images/multihead_chunking.png?raw=1)\n",
    "\n",
    "![](https://github.com/dvgodoy/PyTorchStepByStep/blob/master/images/attn_narrow_first_head.png?raw=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W8LNjGjUNwTu"
   },
   "source": [
    "### Multiheaded Attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "mLiecSFaNwTu"
   },
   "outputs": [],
   "source": [
    "class MultiHeadedAttention(nn.Module):\n",
    "    def __init__(self, n_heads, d_model, dropout=0.1):\n",
    "        super(MultiHeadedAttention, self).__init__()\n",
    "        self.n_heads = n_heads\n",
    "        self.d_model = d_model\n",
    "        self.d_k = int(d_model / n_heads)\n",
    "        self.linear_query = nn.Linear(d_model, d_model)\n",
    "        self.linear_key = nn.Linear(d_model, d_model)\n",
    "        self.linear_value = nn.Linear(d_model, d_model)\n",
    "        self.linear_out = nn.Linear(d_model, d_model)\n",
    "        self.dropout = nn.Dropout(p=dropout)\n",
    "        self.alphas = None\n",
    "\n",
    "    def make_chunks(self, x):\n",
    "        batch_size, seq_len = x.size(0), x.size(1)\n",
    "        # N, L, D -> N, L, n_heads * d_k\n",
    "        x = x.view(batch_size, seq_len, self.n_heads, self.d_k) \n",
    "        # N, n_heads, L, d_k\n",
    "        x = x.transpose(1, 2)  \n",
    "        return x\n",
    "\n",
    "    def init_keys(self, key):\n",
    "        # N, n_heads, L, d_k\n",
    "        self.proj_key = self.make_chunks(self.linear_key(key))\n",
    "        self.proj_value = self.make_chunks(self.linear_value(key))\n",
    "        \n",
    "    def score_function(self, query):\n",
    "        # scaled dot product\n",
    "        # N, n_heads, L, d_k x # N, n_heads, d_k, L -> N, n_heads, L, L\n",
    "        proj_query = self.make_chunks(self.linear_query(query))\n",
    "        dot_products = torch.matmul(proj_query, \n",
    "                                    self.proj_key.transpose(-2, -1))\n",
    "        scores =  dot_products / np.sqrt(self.d_k)\n",
    "        return scores\n",
    "            \n",
    "    def attn(self, query, mask=None):\n",
    "        # Query is batch-first: N, L, D\n",
    "        # Score function will generate scores for each head\n",
    "        scores = self.score_function(query) # N, n_heads, L, L\n",
    "        if mask is not None:\n",
    "            scores = scores.masked_fill(mask == 0, -1e9)\n",
    "        alphas = F.softmax(scores, dim=-1) # N, n_heads, L, L\n",
    "        alphas = self.dropout(alphas)\n",
    "        self.alphas = alphas.detach()\n",
    "        \n",
    "        # N, n_heads, L, L x N, n_heads, L, d_k -> N, n_heads, L, d_k\n",
    "        context = torch.matmul(alphas, self.proj_value)\n",
    "        return context        \n",
    "                                \n",
    "    def output_function(self, contexts):\n",
    "        # N, L, D\n",
    "        out = self.linear_out(contexts) # N, L, D\n",
    "        return out\n",
    "        \n",
    "    def forward(self, query, mask=None):\n",
    "        if mask is not None:\n",
    "            # N, 1, L, L - every head uses the same mask\n",
    "            mask = mask.unsqueeze(1)\n",
    "\n",
    "        # N, n_heads, L, d_k\n",
    "        context = self.attn(query, mask=mask)\n",
    "        # N, L, n_heads, d_k\n",
    "        context = context.transpose(1, 2).contiguous()\n",
    "        # N, L, n_heads * d_k = N, L, d_model\n",
    "        context = context.view(query.size(0), -1, self.d_model)        \n",
    "        # N, L, d_model\n",
    "        out = self.output_function(context)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WPndJg1cNwTx",
    "outputId": "aaab312f-9bd5-41ab-8758-d80ab0c48b93"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([16, 2, 4])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_points = torch.randn(16, 2, 4) # N, L, F\n",
    "mha = MultiHeadedAttention(n_heads=2, d_model=4, dropout=0.0)\n",
    "mha.init_keys(dummy_points)\n",
    "out = mha(dummy_points) # N, L, D\n",
    "out.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rtuAVgk8NwTy"
   },
   "source": [
    "![](https://github.com/dvgodoy/PyTorchStepByStep/blob/master/images/attn_narrow_2heads.png?raw=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZYhQbFhCNwTz"
   },
   "source": [
    "## Stacking Encoders and Decoders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w3Xk9MI_NwTz"
   },
   "source": [
    "![](https://github.com/dvgodoy/PyTorchStepByStep/blob/master/images/stacked_encdec.png?raw=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eAPDj4UeNwT0"
   },
   "source": [
    "![](https://github.com/dvgodoy/PyTorchStepByStep/blob/master/images/stacked_layers.png?raw=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c2eXfMjCNwT0"
   },
   "source": [
    "## Wrapping \"Sub-Layers\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DVCV9AG4NwT0"
   },
   "source": [
    "![](https://github.com/dvgodoy/PyTorchStepByStep/blob/master/images/sublayer.png?raw=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "luPQVuJKNwT1"
   },
   "source": [
    "$$\n",
    "\\Large\n",
    "\\begin{aligned}\n",
    "&\\text{outputs}_{\\text{norm-last}}=&\\text{norm(inputs + dropout(sublayer(inputs))}\n",
    "\\\\\n",
    "&\\text{outputs}_{\\text{norm-first}}=&\\text{inputs + dropout(sublayer(norm(inputs)))}\n",
    "\\end{aligned}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C_Ke5z7WNwT1"
   },
   "source": [
    "## Transformer Encoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mXYoPU3UNwT2"
   },
   "source": [
    "![](https://github.com/dvgodoy/PyTorchStepByStep/blob/master/images/enc_both.png?raw=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zVPRvS0tNwT2"
   },
   "source": [
    "$$\n",
    "\\large\n",
    "\\begin{aligned}\n",
    "&\\text{outputs}_{\\text{norm-last}}=&\\text{norm}(\\underbrace{\\text{norm(inputs + att(inputs))}}_{\\text{Output of SubLayer}_0} + \\text{ffn}(\\underbrace{\\text{norm(inputs + att(inputs))}}_{\\text{Output of SubLayer}_0}))\n",
    "\\\\\n",
    "\\\\\n",
    "&\\text{outputs}_{\\text{norm-first}}=&\\underbrace{\\text{inputs + att(norm(inputs))}}_{\\text{Output of SubLayer}_0}+\\text{ffn(norm(}\\underbrace{\\text{inputs + att(norm(inputs))}}_{\\text{Output of SubLayer}_0}))\n",
    "\\end{aligned}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "FYdXRKjbNwT3"
   },
   "outputs": [],
   "source": [
    "class EncoderLayer(nn.Module):\n",
    "    def __init__(self, n_heads, d_model, ff_units, dropout=0.1):\n",
    "        super().__init__()\n",
    "        self.n_heads = n_heads\n",
    "        self.d_model = d_model\n",
    "        self.ff_units = ff_units\n",
    "        self.self_attn_heads = MultiHeadedAttention(n_heads, d_model, \n",
    "                                                    dropout=dropout)\n",
    "        self.ffn = nn.Sequential(\n",
    "            nn.Linear(d_model, ff_units),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(dropout),\n",
    "            nn.Linear(ff_units, d_model),\n",
    "        )\n",
    "        \n",
    "        self.norm1 = nn.LayerNorm(d_model)\n",
    "        self.norm2 = nn.LayerNorm(d_model)\n",
    "        self.drop1 = nn.Dropout(dropout)\n",
    "        self.drop2 = nn.Dropout(dropout)\n",
    "         \n",
    "    def forward(self, query, mask=None):\n",
    "        # Sublayer #0\n",
    "        # Norm\n",
    "        norm_query = self.norm1(query)\n",
    "        # Multi-headed Attention\n",
    "        self.self_attn_heads.init_keys(norm_query)\n",
    "        states = self.self_attn_heads(norm_query, mask)\n",
    "        # Add\n",
    "        att = query + self.drop1(states)\n",
    "        \n",
    "        # Sublayer #1\n",
    "        # Norm\n",
    "        norm_att = self.norm2(att)\n",
    "        # Feed Forward\n",
    "        out = self.ffn(norm_att)\n",
    "        # Add\n",
    "        out = att + self.drop2(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "8ex52QCuNwT3"
   },
   "outputs": [],
   "source": [
    "class EncoderTransf(nn.Module):\n",
    "    def __init__(self, encoder_layer, n_layers=1, max_len=100):\n",
    "        super().__init__()\n",
    "        self.d_model = encoder_layer.d_model\n",
    "        self.pe = PositionalEncoding(max_len, self.d_model)\n",
    "        self.norm = nn.LayerNorm(self.d_model)\n",
    "        self.layers = nn.ModuleList([copy.deepcopy(encoder_layer)\n",
    "                                     for _ in range(n_layers)])\n",
    "    \n",
    "    def forward(self, query, mask=None):\n",
    "        # Positional Encoding\n",
    "        x = self.pe(query)\n",
    "        for layer in self.layers:\n",
    "            x = layer(x, mask)\n",
    "        # Norm\n",
    "        return self.norm(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "tYcNyRvgNwT4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dvgodoy/anaconda3/envs/pyt20/lib/python3.9/site-packages/torch/nn/modules/transformer.py:286: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance)\n",
      "  warnings.warn(f\"enable_nested_tensor is True, but self.use_nested_tensor is False because {why_not_sparsity_fast_path}\")\n"
     ]
    }
   ],
   "source": [
    "enclayer = nn.TransformerEncoderLayer(d_model=6, nhead=3, dim_feedforward=20)\n",
    "enctransf = nn.TransformerEncoder(enclayer, num_layers=1, norm=nn.LayerNorm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dZHPrqBeNwT4"
   },
   "source": [
    "## Transformer Decoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aMBfkO6KNwT5"
   },
   "source": [
    "![](https://github.com/dvgodoy/PyTorchStepByStep/blob/master/images/dec_both.png?raw=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "IihqH32DNwT5"
   },
   "outputs": [],
   "source": [
    "class DecoderLayer(nn.Module):\n",
    "    def __init__(self, n_heads, d_model, ff_units, dropout=0.1):\n",
    "        super().__init__()\n",
    "        self.n_heads = n_heads\n",
    "        self.d_model = d_model\n",
    "        self.ff_units = ff_units\n",
    "        self.self_attn_heads = MultiHeadedAttention(n_heads, d_model, \n",
    "                                                    dropout=dropout)\n",
    "        self.cross_attn_heads = MultiHeadedAttention(n_heads, d_model,\n",
    "                                                     dropout=dropout)\n",
    "        self.ffn = nn.Sequential(\n",
    "            nn.Linear(d_model, ff_units),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(dropout),\n",
    "            nn.Linear(ff_units, d_model),\n",
    "        )\n",
    "        \n",
    "        self.norm1 = nn.LayerNorm(d_model)\n",
    "        self.norm2 = nn.LayerNorm(d_model)\n",
    "        self.norm3 = nn.LayerNorm(d_model)\n",
    "        self.drop1 = nn.Dropout(dropout)\n",
    "        self.drop2 = nn.Dropout(dropout)\n",
    "        self.drop3 = nn.Dropout(dropout)\n",
    "                \n",
    "    def init_keys(self, states):\n",
    "        self.cross_attn_heads.init_keys(states)\n",
    "         \n",
    "    def forward(self, query, source_mask=None, target_mask=None):\n",
    "        # Sublayer #0\n",
    "        # Norm\n",
    "        norm_query = self.norm1(query)\n",
    "        # Masked Multi-head Attention\n",
    "        self.self_attn_heads.init_keys(norm_query)\n",
    "        states = self.self_attn_heads(norm_query, target_mask)\n",
    "        # Add\n",
    "        att1 = query + self.drop1(states)\n",
    "        \n",
    "        # Sublayer #1\n",
    "        # Norm\n",
    "        norm_att1 = self.norm2(att1)\n",
    "        # Multi-head Attention\n",
    "        encoder_states = self.cross_attn_heads(norm_att1, source_mask)\n",
    "        # Add\n",
    "        att2 = att1 + self.drop2(encoder_states)\n",
    "        \n",
    "        # Sublayer #2\n",
    "        # Norm\n",
    "        norm_att2 = self.norm3(att2)\n",
    "        # Feed Forward\n",
    "        out = self.ffn(norm_att2)\n",
    "        # Add\n",
    "        out = att2 + self.drop3(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "dEtnxSOCNwT5"
   },
   "outputs": [],
   "source": [
    "class DecoderTransf(nn.Module):\n",
    "    def __init__(self, decoder_layer, n_layers=1, max_len=100):\n",
    "        super(DecoderTransf, self).__init__()\n",
    "        self.d_model = decoder_layer.d_model\n",
    "        self.pe = PositionalEncoding(max_len, self.d_model)\n",
    "        self.norm = nn.LayerNorm(self.d_model)\n",
    "        self.layers = nn.ModuleList([copy.deepcopy(decoder_layer)\n",
    "                                     for _ in range(n_layers)])\n",
    "        \n",
    "    def init_keys(self, states):\n",
    "        for layer in self.layers:\n",
    "            layer.init_keys(states)\n",
    "    \n",
    "    def forward(self, query, source_mask=None, target_mask=None):\n",
    "        # Positional Encoding\n",
    "        x = self.pe(query)\n",
    "        for layer in self.layers:\n",
    "            x = layer(x, source_mask, target_mask)\n",
    "        # Norm\n",
    "        return self.norm(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "8syruFiKNwT6"
   },
   "outputs": [],
   "source": [
    "declayer = nn.TransformerDecoderLayer(d_model=6, nhead=3, dim_feedforward=20)\n",
    "dectransf = nn.TransformerDecoder(declayer, num_layers=1, norm=nn.LayerNorm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y4AGPXYHNwT6"
   },
   "source": [
    "## Layer Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dNp8EWjyNwT7",
    "outputId": "67180946-a35d-4ee0-bee1-10c86a2006fa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[-3.8049,  1.9899, -1.7325,  2.1359],\n",
       "         [ 1.7854,  0.8155,  0.1116, -1.7420]],\n",
       "\n",
       "        [[-2.4273,  1.3559,  2.8615,  2.0084],\n",
       "         [-1.0353, -1.2766, -2.2082, -0.6952]],\n",
       "\n",
       "        [[-0.8044,  1.9707,  3.3704,  2.0587],\n",
       "         [ 4.2256,  6.9575,  1.4770,  2.0762]]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_model = 4\n",
    "seq_len = 2\n",
    "n_points = 3\n",
    "\n",
    "torch.manual_seed(34)\n",
    "data = torch.randn(n_points, seq_len, d_model)\n",
    "pe = PositionalEncoding(seq_len, d_model)\n",
    "inputs = pe(data)\n",
    "inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ImijG1DsNwT7"
   },
   "source": [
    "$$\n",
    "\\Large\n",
    "\\overline{X}_{n,l} = \\frac{1}{D}\\sum_{d=1}^Dx_{n,l,d}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0vtGWClmNwT7",
    "outputId": "aa197bee-5e04-4a4b-faff-2372b0d25005"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[-0.3529],\n",
       "         [ 0.2426]],\n",
       "\n",
       "        [[ 0.9496],\n",
       "         [-1.3038]],\n",
       "\n",
       "        [[ 1.6489],\n",
       "         [ 3.6841]]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs_mean = inputs.mean(axis=2).unsqueeze(2)\n",
    "inputs_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "53ISCto4NwT7"
   },
   "source": [
    "$$\n",
    "\\Large\n",
    "\\sigma_{n,l}(X) = \\sqrt{\\frac{1}{D}\\sum_{d=1}^D(x_{n,l,d}-\\overline{X}_{n,l})^2}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WwXyiLX6NwT8",
    "outputId": "8dcb8018-6f72-499f-a779-57557ad3cf1e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[6.3756],\n",
       "         [1.6661]],\n",
       "\n",
       "        [[4.0862],\n",
       "         [0.3153]],\n",
       "\n",
       "        [[2.3135],\n",
       "         [4.6163]]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs_var = inputs.var(axis=2, unbiased=False).unsqueeze(2)\n",
    "inputs_var"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M4bt0D6TNwT8"
   },
   "source": [
    "$$\n",
    "\\Large\n",
    "\\text{standardized}\\ x_{n,l,d}=\\frac{x_{n,l,d}-\\overline{X}_{n,l}}{\\sigma_{n,l}(X) + \\epsilon}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZU-GKwHYNwT9",
    "outputId": "59879b6d-b802-4660-b67a-567fcb35b6e2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[-1.3671,  0.9279, -0.5464,  0.9857],\n",
       "         [ 1.1953,  0.4438, -0.1015, -1.5376]],\n",
       "\n",
       "        [[-1.6706,  0.2010,  0.9458,  0.5238],\n",
       "         [ 0.4782,  0.0485, -1.6106,  1.0839]],\n",
       "\n",
       "        [[-1.6129,  0.2116,  1.1318,  0.2695],\n",
       "         [ 0.2520,  1.5236, -1.0272, -0.7484]]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(inputs - inputs_mean)/torch.sqrt(inputs_var+1e-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zLBLX7RUNwT9",
    "outputId": "6f292d8c-db83-4fd5-ec32-b52e794f88d9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('weight', tensor([1., 1., 1., 1.])),\n",
       "             ('bias', tensor([0., 0., 0., 0.]))])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer_norm = nn.LayerNorm(d_model)\n",
    "layer_norm.state_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ej9uWpuhNwT9",
    "outputId": "95d88b7e-6952-4ef6-9e62-06cd37ad4fc9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor(-1.4901e-08, grad_fn=<MeanBackward0>),\n",
       " tensor(1.0000, grad_fn=<StdBackward0>))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer_norm = nn.LayerNorm(d_model)\n",
    "normalized = layer_norm(inputs)\n",
    "\n",
    "normalized[0][0].mean(), normalized[0][0].std(unbiased=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4EcY83PUNwT-",
    "outputId": "0425aac2-ee2f-4484-9a4f-c2f50e94c469"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('weight', tensor([1., 1., 1., 1.])),\n",
       "             ('bias', tensor([0., 0., 0., 0.]))])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer_norm.state_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ef34JrRgNwT_"
   },
   "source": [
    "$$\n",
    "\\Large\n",
    "\\text{layer normed}\\ x_{n,l,d} = b_d + w_d\\ \\text{standardized}\\ x_{n,l,d}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0HnjDVp_NwT_"
   },
   "source": [
    "$$\n",
    "\\Large\n",
    "\\text{layer normed}\\ x_{n,l,d} = \\text{standardized}\\ x_{n,l,d}\\ \\gamma_d + \\beta_d\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fsXCvrZ0NwT_"
   },
   "source": [
    "### Batch vs Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "URBonuuSNwT_",
    "outputId": "85ecc7c7-31ad-46c7-ba2a-ca8b9a7637de"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[-14.4193,  10.0495,  -7.8116,  ..., -15.4844, -18.0732,  -3.9566]],\n",
       "\n",
       "        [[  2.6628,  -3.5462, -23.6461,  ...,  -0.9403, -18.4375, -37.4197]],\n",
       "\n",
       "        [[-24.6397,  -1.9127, -16.4244,  ..., -17.3708, -26.0550, -14.0706]],\n",
       "\n",
       "        [[ 13.7988,  21.4612,  10.4125,  ...,  -3.2333, -17.0188,   3.9237]]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(23)\n",
    "dummy_points = torch.randn(4, 1, 256)\n",
    "dummy_pe = PositionalEncoding(1, 256)\n",
    "dummy_enc = dummy_pe(dummy_points)\n",
    "dummy_enc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tcxkCEjJNwUA"
   },
   "source": [
    "![](https://github.com/dvgodoy/PyTorchStepByStep/blob/master/images/layer_vs_batch_norm.png?raw=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "id": "VKHBcHcTNwUA",
    "outputId": "0f3f1714-5a73-4bb7-b9b7-4ee897ddf5f7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = hist_encoding(dummy_enc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rPZ9lv8zNwUA",
    "outputId": "8325daff-66e9-4449-8fd6-90eb7a66f317"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[-0.9210,  0.5911, -0.5127,  ..., -0.9868, -1.1467, -0.2744]],\n",
       "\n",
       "        [[ 0.1399, -0.2607, -1.5574,  ..., -0.0925, -1.2214, -2.4460]],\n",
       "\n",
       "        [[-1.5755, -0.1191, -1.0491,  ..., -1.1097, -1.6662, -0.8982]],\n",
       "\n",
       "        [[ 0.8643,  1.3324,  0.6575,  ..., -0.1761, -1.0183,  0.2611]]],\n",
       "       grad_fn=<NativeLayerNormBackward0>)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer_normalizer = nn.LayerNorm(256)\n",
    "dummy_normed = layer_normalizer(dummy_enc)\n",
    "dummy_normed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "id": "1Ayl-iuSNwUB",
    "outputId": "2355cd3f-3c29-4af4-e91c-9d4f525a561a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = hist_layer_normed(dummy_enc, dummy_normed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "znS6-K7gNwUB"
   },
   "source": [
    "### Our Seq2Seq Problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "arObrQGSNwUB",
    "outputId": "7077532f-a51d-4e2b-b5a3-d11bb74d8912"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[ 1.4636,  2.3663],\n",
       "         [ 1.9806, -0.7564]]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pe = PositionalEncoding(max_len=2, d_model=2)\n",
    "\n",
    "source_seq = torch.tensor([[[ 1.0349,  0.9661], [ 0.8055, -0.9169]]])\n",
    "source_seq_enc = pe(source_seq)\n",
    "source_seq_enc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yoEKCgfGNwUC",
    "outputId": "6722aa81-390d-41b3-e635-22f3da27e73a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[-1.0000,  1.0000],\n",
       "         [ 1.0000, -1.0000]]], grad_fn=<NativeLayerNormBackward0>)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm = nn.LayerNorm(2)\n",
    "norm(source_seq_enc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VjctP2zQNwUC"
   },
   "source": [
    "### Projections or Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jpaGfMKSNwUD",
    "outputId": "d1bd436e-e56b-4475-c4e6-03bd8f415dee"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[-2.0934,  1.5040,  1.8742,  0.0628,  0.3034,  2.0190],\n",
       "         [-0.8853,  2.8213,  0.5911,  2.4193, -2.5230,  0.3599]]],\n",
       "       grad_fn=<AddBackward0>)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(11)\n",
    "proj_dim = 6\n",
    "linear_proj = nn.Linear(2, proj_dim)\n",
    "pe = PositionalEncoding(2, proj_dim)\n",
    "\n",
    "source_seq_proj = linear_proj(source_seq)\n",
    "source_seq_proj_enc = pe(source_seq_proj)\n",
    "source_seq_proj_enc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ILNhhrKaNwUD",
    "outputId": "43458dee-98b3-4081-e7be-c33650fda43e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[-1.9061,  0.6287,  0.8896, -0.3868, -0.2172,  0.9917],\n",
       "         [-0.7362,  1.2864,  0.0694,  1.0670, -1.6299, -0.0568]]],\n",
       "       grad_fn=<NativeLayerNormBackward0>)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm = nn.LayerNorm(proj_dim)\n",
    "norm(source_seq_proj_enc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NfRNE7NrNwUD"
   },
   "source": [
    "## The Transformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VXck3Bz7NwUD"
   },
   "source": [
    "![](https://github.com/dvgodoy/PyTorchStepByStep/blob/master/images/full_transformer.png?raw=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "WKwuPjG8NwUE"
   },
   "outputs": [],
   "source": [
    "class EncoderDecoderTransf(EncoderDecoderSelfAttn):\n",
    "    def __init__(self, encoder, decoder, input_len, target_len, n_features):\n",
    "        super(EncoderDecoderTransf, self).__init__(encoder, decoder, input_len, target_len)\n",
    "        self.n_features = n_features\n",
    "        self.proj = nn.Linear(n_features, encoder.d_model)\n",
    "        self.linear = nn.Linear(encoder.d_model, n_features)\n",
    "            \n",
    "    def encode(self, source_seq, source_mask=None):\n",
    "        # Projection\n",
    "        source_proj = self.proj(source_seq)\n",
    "        encoder_states = self.encoder(source_proj, source_mask)\n",
    "        self.decoder.init_keys(encoder_states)    \n",
    "        \n",
    "    def decode(self, shifted_target_seq, source_mask=None, target_mask=None):\n",
    "        # Projection\n",
    "        target_proj = self.proj(shifted_target_seq)\n",
    "        outputs = self.decoder(target_proj,\n",
    "                               source_mask=source_mask,\n",
    "                               target_mask=target_mask)\n",
    "        # Linear\n",
    "        outputs = self.linear(outputs)\n",
    "        return outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "Q2MZn9BoNwUE"
   },
   "outputs": [],
   "source": [
    "class EncoderDecoderSelfAttn(nn.Module):\n",
    "    def __init__(self, encoder, decoder, input_len, target_len):\n",
    "        super(EncoderDecoderSelfAttn, self).__init__()\n",
    "        self.encoder = encoder\n",
    "        self.decoder = decoder\n",
    "        self.input_len = input_len\n",
    "        self.target_len = target_len\n",
    "        self.trg_masks = self.subsequent_mask(self.target_len)\n",
    "        \n",
    "    @staticmethod\n",
    "    def subsequent_mask(size):\n",
    "        attn_shape = (1, size, size)\n",
    "        subsequence_mask = (1 - torch.triu(torch.ones(attn_shape), diagonal=1))\n",
    "        return subsequent_mask\n",
    "                \n",
    "    def encode(self, source_seq, source_mask):\n",
    "        encoder_states = self.encoder(source_seq, source_mask)\n",
    "        self.decoder.init_keys(encoder_states)\n",
    "        \n",
    "    def decode(self, shifted_target_seq, source_mask=None, target_mask=None):\n",
    "        outputs = self.decoder(shifted_target_seq, \n",
    "                               source_mask=source_mask,\n",
    "                               target_mask=target_mask)\n",
    "        return outputs\n",
    "    \n",
    "    def predict(self, source_seq, source_mask):\n",
    "        inputs = source_seq[:, -1:]\n",
    "        for i in range(self.target_len):\n",
    "            out = self.decode(inputs, source_mask, self.trg_masks[:, :i+1, :i+1])\n",
    "            out = torch.cat([inputs, out[:, -1:, :]], dim=-2)\n",
    "            inputs = out.detach()\n",
    "        outputs = inputs[:, 1:, :]\n",
    "        return outputs\n",
    "                \n",
    "    def forward(self, X, source_mask=None):\n",
    "        self.trg_masks = self.trg_masks.type_as(X).bool()\n",
    "        source_seq = X[:, :self.input_len, :]\n",
    "\n",
    "        self.encode(source_seq, source_mask)\n",
    "        if self.training:\n",
    "            shifted_target_seq = X[:, self.input_len-1:-1, :]\n",
    "            outputs = self.decode(shifted_target_seq, source_mask, self.trg_masks)\n",
    "        else:\n",
    "            outputs = self.predict(source_seq, source_mask)\n",
    "            \n",
    "        return outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UoU52fKfNwUF"
   },
   "source": [
    "### Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "9E19-2TjNwUF"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_28051/572022234.py:3: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:275.)\n",
      "  full_train = torch.as_tensor(points).float()\n"
     ]
    }
   ],
   "source": [
    "# Generating training data\n",
    "points, directions = generate_sequences(n=256, seed=13)\n",
    "full_train = torch.as_tensor(points).float()\n",
    "target_train = full_train[:, 2:]\n",
    "# Generating test data\n",
    "test_points, test_directions = generate_sequences(seed=17)\n",
    "full_test = torch.as_tensor(test_points).float()\n",
    "source_test = full_test[:, :2]\n",
    "target_test = full_test[:, 2:]\n",
    "# Datasets and data loaders\n",
    "train_data = TensorDataset(full_train, target_train)\n",
    "test_data = TensorDataset(source_test, target_test)\n",
    "generator = torch.Generator()\n",
    "train_loader = DataLoader(train_data, batch_size=16, shuffle=True, generator=generator)\n",
    "test_loader = DataLoader(test_data, batch_size=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 217
    },
    "id": "sYSUJjm9NwUF",
    "outputId": "a1d960c4-821b-47d4-fdaf-0285406c92d2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_data(points, directions, n_rows=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xJguxfwmNwUG"
   },
   "source": [
    "### Model Configuration & Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "DF56cOibNwUG"
   },
   "outputs": [],
   "source": [
    "torch.manual_seed(42)\n",
    "# Layers\n",
    "enclayer = EncoderLayer(n_heads=3, d_model=6, ff_units=10, dropout=0.1)\n",
    "declayer = DecoderLayer(n_heads=3, d_model=6, ff_units=10, dropout=0.1)\n",
    "# Encoder and Decoder\n",
    "enctransf = EncoderTransf(enclayer, n_layers=2)\n",
    "dectransf = DecoderTransf(declayer, n_layers=2)\n",
    "# Transformer\n",
    "model_transf = EncoderDecoderTransf(enctransf, dectransf, input_len=2, target_len=2, n_features=2)\n",
    "loss = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(model_transf.parameters(), lr=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "V0IgwQWyNwUG"
   },
   "outputs": [],
   "source": [
    "for p in model_transf.parameters():\n",
    "    if p.dim() > 1:\n",
    "        nn.init.xavier_uniform_(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "p4LkgTLCNwUH"
   },
   "outputs": [],
   "source": [
    "sbs_seq_transf = StepByStep(model_transf, loss, optimizer)\n",
    "sbs_seq_transf.set_loaders(train_loader, test_loader)\n",
    "sbs_seq_transf.train(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "id": "lpCLDBgFNwUH",
    "outputId": "000707d5-767a-4bbf-8a9a-46fc68d487f3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sbs_seq_transf.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vNfDOfCUNwUH",
    "outputId": "e4dad5dc-34a6-4fa7-b29a-7fef5ac59414"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.0275, grad_fn=<MseLossBackward0>)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(11)\n",
    "x, y = next(iter(train_loader))\n",
    "device = sbs_seq_transf.device\n",
    "# Training\n",
    "model_transf.train()\n",
    "loss(model_transf(x.to(device)), y.to(device))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "StSL5adGNwUH",
    "outputId": "dcc0f2b7-f89e-4142-975e-49c13dafbefb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.0147)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Validation\n",
    "model_transf.eval()\n",
    "loss(model_transf(x.to(device)), y.to(device))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6iANDCCINwUH"
   },
   "source": [
    "### Visualizing Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 450
    },
    "id": "CQZb7OglNwUI",
    "outputId": "330ec4cb-de59-4ac4-9610-74c5683250a4"
   },
   "outputs": [
    {
     "data": {
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1TgsAmDBhAh5//HFoNBpUVlZi7dq1mDZtGq6++mr07NkTjz32mFNQFVy6dMmn1xEE+qSLKBLIuf8U9nW+7B+l6wWKTqfz+LxwwC7tHeqrQOynhItjDc2YMQMDBw50+Td//nxxuZaKjQDjo/Q24yNFKsYLxgs5MF4wXlDkY7xgvJAD4wXjBVEkYe6C+05XuO8MHt++GUQtoEePHlCpVLDZbGJPmEDzVFKjpXTv3h1WqxXHjh3Dv//9bwwcOBD33ntvk+sJc1scPnwYu3fvxiOPPCLOaZGeni6WzBg8eDBeffVVscyH0Wj0e04Lwbx58zBx4kR89NFH+Oabb/D999/DZDKhoKAA77zzDlasWIHHH38cTz31lLiO9MTw5ZdfxuDBg716rZYon0UU7gKx/wyF/WNLk+6nVq9eLZYobIq7C00AmtVbFghObARC4/NnfPSM8ZH8wXghD8aL34XC58944RnjBfmD8UIejBe/C4XPn/HCM8YLIs+Yu+C+0xXuO4OHSW4KOUlJSejZsycOHDiA48eP48SJE7j88stlfx3pyYI3va4uXrwIAEhNTRUfS01NhVKphN1uF5/3V1paGlauXInRo0fj2LFjeOihh6BQKLyaZ+mGG27A4cOHkZeXB6vV6jSnheD666+HVqsVS4MUFhb6PaeFVNu2bTFr1izMmjVLDO65ubl49913UVlZiZdffhnXXHMNbr31VgD1gU2g0+nQo0cPv1+biJzJuf8U9nW+7B+l6wkUCgUcDoe4v3EnlHo8SvdTSUlJAd1PbdmyxavlWio2AoyPjI8UDRgv5MF4kSLeZrxgvKDIxHghD8aLFPE24wXjBVG4Y+6C+04KLSxXTiFpwoQJAACHw4Hly5cH5DViYmLEALR//36PyxYXF4vzNUh3bBqNRry/b9++Jk+ympKZmYnc3Fx07twZNpsNDz74INavX9/kesKO3mg04sCBA2KgkPY00ul06NOnD4D60iDNmdPCHZVKhT59+uD555/Hhg0bxMc//vhj8fZVV10l9j7Ly8uT5XWJ6Hdy7T+FnpSFhYU4f/68x2WFfWi7du2QmJjo9JxwX1pmyZVjx4752VLvedvz9eqrrxZvh9J+qiViI8D4yPhI0YLxwj3GC+8wXjBeUHRgvHCP8cI7jBeMF0SRhrkL7jspdDDJTSHp/vvvR2ZmJgBg5cqV+Oabb7xe95NPPkFVVZVXyw4bNgwAcPLkSXFOCVdWr17daB3ByJEjAdSfqG3atMnrdrrTMFhMnz7daafrinRui23btjWa00K6HFA/n0Vz5rTwxvXXXy+W6TAYDOLjrVq1Qr9+/QDUf1YXLlyQ/bWJoplc+0/pvu4///mP23X27duH/Pz8RusIOnToAAD46aef3G7j0KFDLufBkVtsbKx422KxuF3u6quvRtu2bQEA//73v2EymQLeNm+0VGwEGB8BxkeKfIwX7jFeMF4wXhD9jvHCPcYLxgvGC6LoxNwF950UOpjkppCk0+mQk5MDlUoFu92O++67Dxs3bvS4zqVLl/A///M/mDRpEurq6rx6nSlTpojzGM2dO9dlT+ADBw5g4cKFAOp34nfccYfT89OmTUNCQgIA4LHHHhN30q6cO3fOq3a1adMGubm5uPzyy2Gz2fDAAw94fP9paWlir6ycnBxYrVZkZGSgW7duTssJgWLPnj3NntPigw8+8Pj/nJeXJ5YHE05CBY899hiA+vJhf/7zn50CSUM2mw3r1q1rsqc3EdWTa/85atQoZGdnAwDefPNNl/MMlZeXY/bs2QDqRzH87W9/a7SMUHrohx9+cHlAXl5ejhkzZvjyFv3WunVr8fapU6fcLqdUKvHoo48CAC5cuIApU6Z4LHdoNpuRk5MDs9ksX2NdaKnYCDA+Sh/zFeMjhQvGC/cYLxgvGC+Ifsd44R7jBeMF4wVRdGLugvtOAfedwcc5uSlk3XjjjViyZAlmzZqF6upq/PWvf8WSJUtwxx134Oqrr0ZqaiqqqqpQUFCA7du347PPPvO5N2z37t0xe/ZsvP7668jPz8fgwYMxe/Zs9O7dG7W1tdi+fTuWLl2K6upqKBQKvPHGG4iJiXHaRkZGBhYuXIipU6eitLQUN998M+6//37cfPPNyMzMhNlsxtGjR/HVV1/hs88+82oODaA+WHz66ae47bbbcOLECUydOhUKhQJ33XWXy+VvuOEG/Prrr6isrBTvN9SvXz/ExMQ4/T/5GyimT5+OZ599FqNGjUK/fv1w2WWXITY2FgaDAXv37sU777wDAFCr1Zg0aZLTuiNGjMDMmTOxZMkS7N+/H9dffz0mT56MQYMGQa/Xo6amBmfOnMH333+P3NxcXLx4EXv37hVPiInIMzn2nxqNBm+88QbGjh2LqqoqjBo1Cg8++CBGjBiBmJgY/PTTT1i0aBEKCgoAAA8//LBTGT7B5MmTsXLlStTV1eG+++7DY489hkGDBsFms2H//v1YtmwZSkpKcM0113g80JZDu3btkJ2djfPnz2Px4sXIyspCly5doFKpAADp6eli+cO//OUv2LlzJz755BN8/vnn4n6qb9++SElJQVVVFU6cOIG8vDx8+umnqKiowPjx4wPafqBlYiPA+OhuOW8wPlI4YbxwjfHCe4wXjBcUHRgvXGO88B7jBeMFUaRh7oL7Tu47QwOT3BTS7rvvPnTq1AmPP/44Dh48iB9//BE//vij2+X1ej2eeOIJJCUlef0azz77LKqrq7F8+XIUFBSIvWulYmNj8cYbb+CPf/yjy22MHTsWADB79mxUVVVh5cqVWLlypddtcEfoFXXbbbfh5MmTYrC48847Gy17ww034O2333a67+p99OnTB3v37gXQ/DktSkpK8N577+G9995z+bxOp8Mbb7yBa665ptFzCxYsQFpaGv75z3/CYDDg1VdfxauvvupyO1qt1qkMGBE1TY7950033YScnBw8/PDDqKqqcvs7nTp1Kp5//nmX2+3WrRsWLFiAJ554ApWVlXj22Wedno+Li0NOTg4+++yzgF+EAup7vj766KM4c+ZMo4tGS5cuFedVUigUWLlyJTIzM/HOO+/g3LlzmD9/vtvtxsfHixezAq0lYiPA+Mj4SNGC8cI1xgvvMV4wXlB0YLxwjfHCe4wXjBdEkYa5C+47Ae47g41Jbgp5/fv3xzfffIMvv/wSn3/+Ofbt24eLFy+ivLwccXFxaNOmDXr16oU//vGPGDVqVKPeSk1RKBT45z//ibvvvhsrV67E3r17UVxcDLVajXbt2mHYsGGYPn062rdv73E7Y8eOxdChQ/HOO+/g66+/xsmTJ1FZWYn4+HhcdtllGDhwoBhQfJGVlSX2ijp58iT+9re/QaFQNCo9Isxt4XA4xPuuDB48WAwUzZnTIi8vD19++SXy8vJw+vRpFBcXo6KiAnFxcbj88ssxdOhQ/PWvf0W7du3cbmPu3LkYO3Ys3nvvPezcuROnTp1CZWUlYmNjkZmZiSuvvBJDhw7F7bffDr1e71c7iaKZHPvPsWPHYsCAAVi+fDm2b9+OgoICWCwWZGRkYODAgfjrX/8qzlXjzgMPPIArrrgCS5cuxQ8//ACTyYSMjAwMGzYMDz/8MLp27YrPPvssUP8NTqZMmYL09HS89957+OWXX1BWVgar1epyWbVajZdffhmTJ0/GqlWrsHv3bhQUFMBoNCI+Ph7Z2dno2bMnhg8fjlGjRkGn07XIewACHxsBxkfGR4omjBeNMV54j/GC8YKiB+NFY4wX3mO8YLwgikTMXXDfyX1ncCnKy8sdwW4EERERERERERERERERERGRN5TBbgAREREREREREREREREREZG3mOQmIiIiIiIiIiIiIiIiIqKwwSQ3ERERERERERERERERERGFDSa5iYiIiIiIiIiIiIiIiIgobDDJTUREREREREREREREREREYYNJbiIiIiIiIiIiIiIiIiIiChtMchMRERERERERERERERERUdhgkpuIiIiIiIiIiIiIiIiIiMIGk9xRzGw24+TJkzCbzcFuCrnBzyj08TOiSMfveOjjZxT6+BlRNOD3PPTxMwp9/IwoGvB7Hvr4GYU+fkYUDfg9D338jEJfNHxGTHJHOZvNFuwmUBP4GYU+fkYU6fgdD338jEIfPyOKBvyehz5+RqGPnxFFA37PQx8/o9DHz4iiAb/noY+fUeiL9M+ISW4iIiIiIiIiIiIiIiIiIgobTHITEREREREREREREREREVHYYJKbiIiIiIiIiIiIiIiIiIjCBpPcREREREREREREREREREQUNpjkJiIiIiIiIiIiIiIiIiKisMEkNxERERERERERERERERERhQ0muYmIiIiIiIiIiIiIiIiIKGwwyU1ERERERERERERERERERGGDSW4iIiIiIiIiIiIiIiIiIgobTHITEREREREREREREREREVHYUAe7AURE0Sg/Px95eXnYsWMHTp06BbvdDqVSiU6dOmHYsGEYMGAAunXrFuxmEhERERERERERERERhRwmuYmIWkhNTQ3WrFmDnJwclJaWorS0FHa73WmZgwcPIjc3F3q9HmlpaZg6dSomTJgAnU4XpFYTERERERERERERERGFFpYrJyJqAXv27MGAAQPw9NNP4+jRo7h06VKjBLfAbrejpKQE+fn5ePrppzFgwADs2bOnhVtMREREREREREREREQUmpjkJiIKILvdjlmzZmHixIk4ffo0amtrfVq/trYWp0+fxsSJEzFr1iy3iXEiIiIiIiIiIiIiIqJowXLlREQBYrfbMXHiRGzfvh3V1dXN2pbBYMD69ethMBiwevVqKJXso0RERERERERERERERNGJSW4iogCZM2eOLAluQXV1NbZv3465c+di0aJFsmyTSA75+fnIy8vDjh07cOrUKdjtdiiVSnTq1AnDhg3DgAED0K1bt2A3k4iIiIiIiIiIiIgiBJPcREQBsHv3bnz66ac+J7jnzZuHl156CYcOHULPnj0bPV9dXY3c3FyMHTsWgwYNkqu5RD6rqanBmjVrkJOTg9LSUpSWljYqp3/w4EHk5uZCr9cjLS0NU6dOxYQJE6DT6YLUaiIiIiIiIiIiIiKKBExyEwCOwiOSU01NDWbOnAmDweDTetnZ2Zg3bx5MJpPH5QwGA2bMmIG8vDwmCyko9uzZgxkzZqCwsLDJeebtdjtKSkpQUlKCp59+GkuWLMHSpUvZSYOIiIiIiIiIiIiI/MYkdxSrqanBhg0bsHHjRpSVlXEUHpFM1qxZg8LCQp/Xe/XVV7Fv3z6oVCq0atXK47JFRUVYu3YtpkyZ4m8ziXxmt9sxZ84cfPrppz534gCA2tpanD59GhMnTsRtt92GhQsXcn55IiIiIiIiIiIiIvIZryxHqT179mDo0KFYuHAhjh07hkuXLjVKcAuEUXj5+fl4+umnMWDAAOzZs6eFW0wUPnJycpoc3drQ4MGDcc8992D27NleLW82m5GTk+NH64j8Y7fbMXHiRKxfv96vBLeUwWDA+vXrMXHiRLexh4iIiIiIiIiIiIjIHSa5o4zdbsesWbMwceJEnDlzBhaLxaf1paPwZs2axeQEUQP5+fkoLS31aR2lUonFixdjxYoVOHTokNfrlZaWIj8/39cmEvnl8ccfx/bt232eZ96d6upqbN++HXPnzpVle0REREREREREREQUPZjkjiIchUcUeHl5eT7/vqZPn44OHTrg2Wef9Wk9g8GAffv2+bQOkT/279+Pzz77zKcE97x58+BwOPDLL7+4Xaa6uhq5ubmsDkJERETkwrZt2zBt2jR0794dmZmZ4l/37t0xbdo0bNu2LdhNJCIiIiIiChrOyR1F5syZE7BReIsWLZJlm0ThxOFwwGq1ora2FiaTCWazGdu2bYPD4fB6G2lpaXjxxRcxf/58XLp0yafXt9vt2LFjByZNmuRr04m8VlNTg/nz5/tUoSA7Oxvz5s2DyWRqclmDwYAZM2YgLy8POp2uOU0lIiIiCnsVFRV46aWX8Nlnn6Gurs7luUVhYSE+/PBDrF+/HhqNBqNGjcJrr72GtLS0ILSYiIiIqPny8/ORl5eHHTt24NSpU7DZbLBarejSpQtuuukmDBgwAN26dQt2M4koxDDJHSW+/vprbNiwwWOCu0ePHnj++efRp08fZGZmorq6GocPH8Yrr7yCTz/91OU6wii8sWPHYtCgQYFqPpFX7HY76urqYLVaUVdXB5vNJt6X/jV8rqn7Ta1bWVmJpKQkqNVq/Pjjjz61ecGCBSgtLcXixYv9es+nTp3yaz0ib61btw4lJSU+rfPqq69i3759UKlUaNWqVZPLFxUVYe3atZgyZYq/zSQiIiIKe++88w6ef/552Gw2r5Z3OBywWCzYtGkTcnNzsWDBAkyfPj3ArSQiIiKSR01NDdasWYOcnByUlpaitLS0UdXY/Px8fPbZZ9Dr9UhLS8PUqVMxYcIEDpQgIgBMckeF7du3495774XVavW4XIcOHZCYmIhVq1bhwoULiIuLw913343c3FxMmzYN77zzjsv1OAqPXBFGOfuTZJYu5+q+u2VDoXS+L6O4O3fujGnTpmH27NnIysoSH4+NjYVGo0GHDh1QWVmJsrIyt9vw9gIYkb9WrlwJi8Xi9fKDBw/GPffcg969e3vdecNsNiMnJ4dJbiIiIopKVqsVAwYMwPHjx306n2i4jaeeegorV65EXl4e1Gpe7iEiIqLQtWfPHsyYMQOFhYWora31uKzdbkdJSQlKSkrw9NNPY8mSJVi6dCkH3RERk9yRzG63Y86cOdiwYUOTCW4A2Lp1K7Zu3er02JIlS7B//37MnTvXbZIb4Ci8UOdwOMQSL94kkT3dF7bT1LLefOfCiVqthkajgUqlgkajgVqthlqthsPhQGVlJVq3bg2dToe4uDivt5mdnQ2VSoXFixe7TAaePn0aixYtwpw5c9xuQ6VS+fV+iLyRn5/vU5lypVKJxYsXY8WKFTh06JBPr1VaWor8/HyWniIiIqKoYrVaceWVV+LixYvN3pbD4cCxY8dw1VVX4dChQ0x0ExERUcgRchaffvopDAaDz+vX1tbi9OnTmDhxIm677TYsXLgQSqUyAC0lonDAM54IZbfbMXHixGbPwW2321FQUIC+fft6XI6j8Hxjt9u9ShRXVVXh/PnzKC4uhlKpbDLJ7Gl0tL8jAkKRSqUSk8zCX8MEdFP3fVlXrVZDoVC4bIvZbEZBQQHatWuH2NhYbNy4EWfOnPHqfRw6dAh33nlno8cXLFiAxMREzJo1CydOnPC4jU6dOnn1WkT+yMvL8ynJPX36dHTo0AE33XSTz69lMBiwb98+JrmJiIgoqgwYMECWBLdUUVERBg4ciO+//17W7RIRUWA0nIvYbrdDqVSiU6dOGDZsGOcipoghV84CqL+OtH79ehgMBqxevZqJbqIoxSR3hJozZ47fwSIuLg46nQ7Jycm4/fbbccstt2DdunVNrheuo/CEstr+zNXsz7zOvpTVtlqtTvM9hyKFQuFTUtmbJLO7ZYX7oXzQMmzYMGzevNmrTgUGgwGffPJJo8dnz54NAC6fk1IqlRg2bJhf7STyxo4dO7zuIJOWloYXX3wR8+fPx6VLl3x+Lbvdjh07dmDSpEk+r0tEREQUjpYtW4bjx497XCYjIwMffvghevToAb1eD6VSiRUrVmDq1Kke1zt27BiWL1/OObqJiEKUN3MRHzx4ELm5uY3mInY3EIMo1DUnZ+FKdXU1tm/fjrlz52LRokWybJOIwktoZs2oWXbv3o1PP/3UY7Do0aMHnn/+efTp0weZmZmorq7G4cOH8corr2DUqFHiibDNZsPGjRsxc+bMJl9XjlF4DocDdrvdr7mZmzOvcyRxlUBuKsns7bKu7iuVSh5cSwwYMAB6vd6vJJ+v9Ho9+vfvH/DXoeh16tQpr5ddsGABSktLvZ6Hu7mvR0RERBTOSktL8dxzzzXZobBLly4YMmQIrFYrKioqkJqa6tX2HQ4HnnnmGYwbNw5paWlyNJmIiGTS3LmIFy5ciLZt27ZQa4nk4U3OwpV58+bhpZdewqFDh9CzZ89Gz1dXVyM3Nxdjx47lHN1EUYhJ7ghTU1ODmTNnNjmfRYcOHZCYmIhVq1bhwoULiIuLw913343c3Fw899xzuOmmm5CVlYVx48ZBpVJBq9U2+dp2ux0fffQRunbt2qx5nSOprLZSqXSZGPZUNlt63263w2AwoG3btoiLi3M7Wlq6LhPOwdWtWzekpaU1K8nt7ejstLS0sKucQOHF26oTnTt3xrRp0zB79mxkZWWJj8fGxkKj0aBDhw6orKxEWVmZx+2UlZVhy5YtSExMRFJSEhISEpCYmIjExETodDru34iIiChiPProo151uP7555/Rs2dPHDp0CPfffz/+/e9/e/0aVqsVjz32GFauXNmcphIRkUzkmot4ypQpuPHGG7Fs2TIALHdOoc/bnEVD2dnZmDdvHkwmk8flDAYDZsyYgby8POh0uuY0lYjCDJPcEWbNmjUoLCxscrmtW7di69atTo8tWbIE+/fvx/jx4zF//nwAwL///W98/vnnyM3NRb9+/Zrcbn5+Pnbu3OlX2wOtqbLagZjXublltRvO90zhYdq0aXj66aeb7I3bHLGxsZg2bVrAtk8EwOt9WHZ2NlQqFRYvXuxyJPfp06exaNEizJkzx+N26urqcPDgQZfPqdVqMek9aNAgp/norVYrqqqqkJCQAJVK5VWbiYiIiIJpy5YtXi1nMplw6NAhv18nNzfX73WJgmHbtm3YuHEjdu3a5dRJNjU1FYMHD8aYMWMwcuTIILaQyD9yzkVcWlqKbdu24aabboLVavW53DmTgNTSvM1ZNPTqq69i3759UKlUaNWqlcdli4qKsHbtWkyZMsXfZhKFDR4v/Y5J7giTk5Pjd2LNbrejoKAAffv2dXp8w4YNyMnJQdeuXXH06NEmt+Etd+Wy5ZrXueF9lUrFUYDUIiZMmIAlS5bg9OnTAXuNzMxMjB8/PmDbJwKATp06uU06Sx06dAh33nlno8cXLFiAxMREzJo1CydOnGhyO0lJSW6fs1qtKC8vR3l5eaNRT4WFhfjPf/4DhUKB+Ph4JCYmiglx4V9hZLher2csIL9xhAQREclh27ZtqKura5HXqqurw7Zt26LmIheFp9LSUjz66KPYsmWL2wp/hYWF+PDDD7F+/XpoNBqMGjUKr732GsvxU9iQey5is9mMw4cPN7mcq3LnS5cuZVlnalH+5CwGDx6Me+65B7179/Zqajyz2YycnBwmuSli+XO8dOutt2LmzJlo165dEFrcMpjkjiD5+fkoLS31aZ24uDjodDokJyfj9ttvxy233IJ169Y5LSP07ktOTm5ye6mpqRgzZoxX8zozyUCRSqfTYfHixRgzZkxALl7p9XosW7aMPW8p4IYNG4bNmzc3OY2EwWDAJ5980ujx2bNnA4DL5xpSKBT429/+httvvx1GoxEmkwmVlZUwmUwwGo3iYzU1NUhMTHRa12g0Aqife9JkMrktY6VQKPD44487xZ8jR46guLhYLIsuJMXj4+MZpwhAfVm1NWvWICcnhyMkiIhIFhs3bmyxabocDgc2bdrEJDeFrOXLl+OZZ57xqnw/UP+dtlgs2LRpE3Jzc7FgwQJMnz49wK0kap5AzUXsK6Hc+cSJE3Hbbbdh4cKFza5CSdQUf3IWSqUSixcvxooVK3yqaFNaWor8/Hx2PqeI4+/x0scff4zc3Fw8//zzePjhhwPcyuBgkjuC5OXl+TyvxWuvvSaeDNhsNmzcuBEzZ84Un1er1Zg4cSKqq6u96h3Yo0cPBhEi1FdACMSFK5VKhdGjR2PgwIGyb5sil7+jTwcMGIC0tDS/5grzVatWrXDjjTeidevWaN26tdvl6urqGpUkj4uLQ7du3ZyS4a5+f/Hx8Y1O4I8dO4Zff/210bJKpdJpTvBOnTqhV69eTsvYbDaWR49we/bswYwZM1BYWNhkr3OOkCD6HUunEXn27bfftujr7dq1q0Vfj8gbVqsVffv2xfHjx/0+d7ZarXjqqaewcuVK5OXlQa3mZU4KPYGei9gfBoMB69evh8FgwOrVq5nopoDyJ2cxffp0dOjQATfddJNP6xkMBuzbt4/5CYoYVqsVAwYMaNbxks1mw3PPPYfVq1dH5PFSZL2bKLdjxw6fv+iLFi3Chg0bkJWVhX/9618YMmQInnrqKRw9ehSZmZmYMGECunfvjrlz56KqqsrjtpRKJYYNG9act0AUEYQeut72rAK8752rVCpx9913y9FMinByjD7t1q1bs5LcvsSEtLQ0r05CNBpNo8c6duyIjh07ivftdrs4olua+HZ14i6MAm/IbrejsrISlZWVAOByRO6bb77ZKBnesDx6YmIiYmNjOSo8zNjtdsyZMweffvqpX9//hiMk/vGPfwSglUShpaysDPPmzcPOnTthtVpZapYimtVqhdlshsVigdlsRm1trfivcLvhc9L7ly5datH2SjubEIUCq9WKPn364OLFi83elsPhwLFjx3DVVVfh0KFDEXfhlsJfS8xF7I/q6mps374dc+fOxaJFi2TfPpHA15xFWloaXnzxRcyfP9/nYya73Y6vvvoKt9xyi9OUpsK0pkThxGq14sorr+TxUhPC+p2sW7cOeXl5OHDgAA4fPgyLxYKlS5diwoQJPm3HbrfjnXfewapVq3Dy5EnEx8dj6NChePbZZ50umoe6U6dO+bxOfn4+8vPzAUD8/5s9ezYUCgWMRiP279+PJ554Arm5uU1uKy4uDsXFxXj//ffFObBVKhXatGmD66+/3mnZn376CRaLxWk54bZarYZSqYRarUZKSgoSEhLE9ex2O+rq6sRlmDSgUONPD11feufW1dXh4YcfRl5eHsvg+iDa4oWco0+nTJmC5557DhaLJWDtjY2NxbRp02TbnlKpRFJSksc5vgW33HILysvLnZLh0ttCBy9pLAIgXqgG6i8OFBcXu32NsWPHonPnzuL98vJyHD9+vNHc4ew9HxrsdjsmTpwoy3x5wgiJ4uJiPP/88/I0kAIm2mKFnFhqlsKJ3W4Xk9HeJKSlywm3fenM6kpLlSoP1utFOsaL5vvTn/4kywVbqaKiIgwcOBDff/+9rNslaq6WmIvYnaYGVFRXVyM3Nxdjx45lBaoAYLyo52vOYsGCBSgtLfX7u3/s2DGsXLmy0eMqlcop8a3VajF58mSn/MLhw4dx7tw5aLVapwS59LZGo0FCQgL0er3T9h0OB3MVJKsBAwbweMkLYZ3kXrBgAQoKCqDX69G6dWsUFBT4tZ3Zs2dj9erV6N69Ox544AEUFhbi448/xvbt2/HVV1/h8ssvl7nlgdFwhJ6v1q1bh6SkJOTk5KBbt244evSoT+vHxMTAarXi9OnTTo/X1dU1SnLv27cP5eXlTW7z5ptvRp8+fcT7FRUVWL58uXhfmhhvmCwfM2aM0zzip0+fxi+//OJ2HeG+TqfDFVdc4dQOg8EAi8XicT0GMQL866Hra+/coqIirF27FlOmTPG3mVEnWuKF3KNPFy5ciHvvvReLFy/G+fPnA9DiepmZmRg/fnzAtu9JWlqaxxGEVqsVJpOpUQ/H2tpaZGdni0lxTzG44Rzi58+fx5dffun0mEKhQHx8vNMI8OTkZPTr18+Pd0XNMWfOHFkS3ILq6mp88803+Oc//4m33npLlm1SYERLrJCTHKXTWGqWfOFwOMRR1E0lpN0952uiQS4qlQqxsbGIiYlp8XNHnqvKi/GieXJycnD27FmPy2RkZODDDz9Ejx49oNfroVQqsWLFCkydOtXjeseOHcPy5cvZcYpCRkvORdyQtwMqDAYDZsyYwcEUAcB4Uc+XnEXnzp0xbdo0zJ49G1lZWeLjsbGx0Gg06NChAyorKz1WqXHXGdFms8Fms4n3NRpNo2OkM2fO4MCBA022s2vXro0qbS5fvhwmk8llUlyaLL/mmmucOieYzWYcOXLEKfnuKsEuPM7juuiwbNkyHD9+3OMyPF6qF9ZXEBYvXozLLrsM7du3x8KFC/HCCy/4vI1vv/0Wq1evxsCBA/Hxxx9Dq9UCqB91NXbsWDz22GPYuHGj3E0PCDlGgAkHM9LksDfUarVTMlrKVSkQaUDxpOG6DdezWq2wWq0uL1Q0vNBWUlLi1cFhWlpaoyT3119/jRMnTjTZ1j59+mDEiBFOj69atQoKhaJREr7h6PVrrrnGKcFpNBrx22+/4dKlS6iurkZ8fLzL9VQqFZKTkxngQoSvPXT96Z1rNpuRk5PDJLcPoiFeBGL0qcFgQE5ODp599lnMmzfP55Nzb+j1eixbtixkT6aFqiINJSYmYuLEiQDq401VVZWY8DaZTKisrBRvN0xyu7rI4HA4xBLr0tdomOTeunUrzp0751QePSYmBiaTCVqtFq1atUJcXBxjgp+E6SY8/YZ69OiB559/Hn369EFmZiaqq6tx+PBhvPLKK/j0009drlNdXY0dO3YgLy+PU7uEsGiIFXJi6TTyh91ub1TW29vR08L95nbu9odCoUBMTAy0Wq2YqI6NjW10PyYmxu1z0u/1okWL/Cpd66/U1NQWe61owHjhv9LSUrz44otNdozq0qULhgwZAqvVioqKCq+/ww6HA8888wzGjRvHqTAoJLTkXMQN+TKggoMpAoPxop4vOYvs7GyoVCosXrzY5XXS06dPY9GiRZgzZ47bbWi1WlxzzTWoq6uD1WpFXV2deNtisYi3XZ1z1NXVedVOV+sKeYqmKv40HH1vNBqxbds2r173oYcecsrb/PLLL/jhhx/cJsiFJHliYiKuvvpqp21dunQJVqu1UVKeifTgKy0txXPPPcfjJS+F9dWDoUOHNnsbq1evBgA8/fTTYpAAgD/84Q+44YYbsH37dhQUFKBdu3bNfq1A69SpEw4ePOjVsunp6SgpKXF6TK1WY+LEieLFWl+0bdsW7733njia22azwWq1wm63uwxkt956K2pra8XlhJ5UDW+np6c3amPHjh0bLe9qGw2Djb+JdW/XdbWMw+HAhQsXvHrdTp06OR10lpSUYNu2baisrERSUpLHi31PPvmk0/0dO3Zg//79bhPqwmNZWVkYMmSI07r79u2D2WwWS8a7S8xnZGQ4JX5sNhsqKytdlp6PlsDoaw/d5vTOLS0tRX5+vldzGFN0xItAjD7dvn07nnjiCTzyyCO49dZbsXHjRtm2D9RPczF69GgMHDhQtm0Gg0KhQEJCAhISEpCZmdnk8l26dEFcXJzLEulVVVXiQayrcusGgwGXLl1ympfKarWisrJSHAGpUqnQr1+/Rvv33377DfHx8WKCnPNROfN2uokOHTogMTERq1atwoULFxAXF4e7774bubm5mDZtGt555x2X65WXl2P27NnYt2+f15068vPzkZeXhx07duDUqVPicVWnTp0wbNgwDBgwgHFARtEQK+TE0mnRRygz70tCuuFtby8cyk2j0XhMQEvvS5eT3pfznGbw4MH48MMPvV5+3bp1SEtLE48zbrnlFrEqzOTJk3Hu3LkmX4/kw3jhv0cffdSrcv8///wzevbsiUOHDuH+++/Hv//9b69fw2q14rHHHnNZqpZILg6HAw6HAzabDXa7vdG/wu2tW7e22FzEUr4OqOBgisBgvKjnS87i0KFDuPPOOxs9vmDBAiQmJmLWrFlNDkTr2rUrbr31Vn+aiiFDhuC6665zmRSXJssb5iyA+nxLfHy8uJzw1zBnoNFonO77cnzccF2j0YiioqIm12vdunWjJPcXX3yBM2fONFpWoVA4JbyvvfZa9O/fX3ze4XDg008/hUajgcPhQFlZGS5cuID4+PhGCfbMzEyn6x/C/pFVcT3j8ZJvwjrJLYfdu3cjPj7e6YcqGDFiBHbv3o09e/bgT3/6UxBa55thw4Zh8+bNXh08vf3220hKSsK3336L8+fPIzMzExMmTED37t0xd+5ccQ5Sbwij8OLi4gC4ThI3dNlll3m9famUlBTcd999fq3bq1cvdOnSRUyEC0l44bbweExMTKN1u3btCr1e7zKZLn2sYULCarVCpVJ5lSRv+P/m7TxvroKCEICbCpKuEuc///yzV4nam266CX379hXvG41Gp1LyAmEUuzRRPn78eKeeRcePH8ePP/7oNqEu3I6Li0Pv3r2dtn/hwgXU1NS4LCEv3YZGowl4QsfXHrrN6Z1rMBiwb98+JjdaUCjHC29Gn7rizfxcW7ZswaBBg/Cvf/0L5eXlsiXS4+LiMHz4cLz++uvN3la48VQi3WaziSO6XcVzYb/mKUbYbLZGJz61tbXYtGmT02NCwlsojy7cFpLw0cbb6Sa2bt2KrVu3Oj22ZMkS7N+/H3PnznWb5Aa8GyFRU1ODNWvWICcnB6WlpSgtLW00avHgwYPIzc2FXq9HWloapk6digkTJoRsRYRoEsqxQk7elE6bOHEiHn30UXTt2hUxMTGw2WwoLCzElClTGk3ZIBVppdNCidVqRU1NjXisLySqXSWn3c1RHYy5nRUKhU8JaVfPhVrHrjFjxmD9+vVe/3+OGTPG6dwtOzsb2dnZAOo7X3lKcisUCtx1113NazDJLlriRUNbtmzxajmTydSsMs25ubl+r0uB53A4XCaGGyaIhX9tNlujhLL0trvnpNtq6jV8fX273e7VPvynn37y6f+muXMRA/4PqOBgitAUCfHCl5yFwWDAJ5980ujx2bNnA4DL56SUSmWzqqclJyf7XOFW4O4zsNvtTgnyhuftycnJGDVqlFNiXJpUl/5JOzoI21YqlU1WOnI38twVoWOrxWIBAPFfQV1dnbhvEQZdnDhxwuVrTJgwAe3btxfvHz9+HB999BGUSqXL+c6F2zExMbjjjjuctnXq1CkYDAaXJeClo9iF6kvhjMdLvonqJHdVVRWKiorQo0cPlye9QiK2qd5BQH2Pt2C79tprkZaW5lWSbd26dZgyZQoefPBB6PV6GI1G7N+/H0888YRPX+64uDjceuutuPbaa0Pi/6Ap8fHxXi3X8L1ceeWVXr9Gw3UfeeQR8WDY06j15ORkp3WTk5MxbNgwlJSUiOXIXa3r6jW1Wi1SUlKclpcm9AU2m63Rumaz2asEe8N1q6qq3K7XMNkuXCwTFBcXIz8/v8nXTE1NRffu3Z0e++abb5q8yAoA11xzDW6++Wanx1asWAEATqPWGybXVSoVrrvuOrRu3Vpcr7KyUpwrxW63o6KiAmVlZdi0aZPXF6qa2zvXbrfj66+/xr333uvzukD9XDbkvVCOFzU1NZgxY4bPJdC8nZ+rtLQU8+fPx/Dhw5GTk4MnnngCW7ZsaVbp8rS0NIwaNQovv/xyo4NlgnhRHmj8fbnrrrvgcDhgNpvFEeBlZWUoKCiAWq1GbW0tjEYj4uLinNYVylBJVVRUoKKiotHr/+Uvf3GqwnLixAn88MMPYhJcSIoL/8bFxckyZUqwvf32237P02q321FQUODU+cuV2tpaLF++HBMmTHD5fF5eHmbPno3CwsImfxt2ux0lJSUoKSnBvHnzsHjxYixatAgDBgzw6z24w3jhPTljBRAa5xeulJWVeVU67aWXXkKbNm3wyy+/4Oeff0Z2djaGDRuGzz//HHfddZfbC1RC6bQ77riDJZYlHA5Ho3ml/fmrqqoSp0FqKVqttlGp74b3PT3nar5EX3jT+belDR06FGq12ut2Ney85gu1Wo2hQ4cGdJ/CWOGbaIkXDX3xxRct9lusq6vD5s2bG10DiATC9SVPidyGidqGSdumtmGxWGAwGPDbb79BoVC4TQo3vN1UUlh6O1r40jlMjrmIAf8HVBgMBuzatQsdOnTwaT1fMF74JlLihS85i+ZKS0tD7969QzI2CgOzhGs6ApVKha5du3q1jYbX9q+77jpcd911sNlsTqPPhSS58K9Go2n0f9KpUyekpqY2Sqw3TLArlUq3OQAhN+FugJ8wVZGrdT1d84iNjW3U3p9//hm//PJLk/9HXbt2bZQgX7t2LSwWi8v50oX7arUaXbp0QUZGhriexWJBYWGh28R6IK6F8Xipni/xIqqT3JWVlQBclwOVPi4s58mFCxe8LocdKDqdDomJiV4nudetW9fs17v++usxc+ZMFBQUNGtbhEbl44H6UiLS5Ko7Df//27Zti7Zt27pcVtpj1tW6ffv2FUupSP+kJyvCCYt0XZPJhIyMDDGh7mp9ITlfVFQEo9EornvhwgWvfmdKpbJRe4uLi71aV0gCSf8fXJVkcSUlJcUp8J47dw6fffZZo+V86TklR+/c/Px8v357KpXK72oK0SqU48WGDRv8mtPRl/m5SkpKsHLlStxzzz145JFHMGjQIMyfPx8lJSU+Jam1Wi3S09Px7LPPok+fPjh//rzP7SZnGo0GGRkZTgfhAun+obq6Gt27d4fJZEJ1dTWqqqpQVVWF6urqRhdeysvLnU4mDh8+7HH/plAoEBcXh/T09EYHxmVlZVAqlWLZqlB16tQpnzscxcXFQafTITk5GbfffjtuueUWr46tLl26hG+//RadOnUSH7Pb7fjHP/6BnTt3ory83Nfmw2Kx4MyZM5g8eTKGDh2Kp556SpaTLcYL38gZK4DQOL9wZd68eV51iHzxxRexZs0ap+ofI0aMwJdffomFCxd6HIVhtVrxyCOP4O9//7ssbQ424UK/UK67trYWFotFvC+9LTwn/autrYXVapVtFLUvVbuUSqWYaBaS1cLoCq1W6/Tn7jlf90fCxbBQvDgpp6FDh3qsaiDn6wTyfJ2xwnfREi8aWrt2bYtVg3A4HPjggw+cOskL5aUbJnob/itN2EoTs9Ll3G3D07rebNfdNqR/waioEeqEz7apZRQKhVNMEv7PpRQKhbic9E9Iagj3hdcUlhWqGAL1+0XhdXw5B5JjLuLmDKiw2+3Ytm1bo2mv5MJ44btIiRe+5Czc8XZ0dmJiInQ6HXMV/59wLA80zgFkZmZ6NeVew3XtdjtGjhzpNAd5wz8hWV5RUeGUsC0rK0NcXJzTstIEvcBqtTZqb1FRkVff9YqKikbrnjx50qtBDWaz2anDwaVLlzzOeS+ND2PGjHEapX/q1CmcPHlS7Nzg6k+j0UCn06FNmzbiesE+XgoFvsaL0L3aGGakPeyCafr06XjuuecCPjJOmH/y5ZdfjojRW6HKYrHg4sWLaN26dYuV2WjOHC7+7hCzsrIwYsQIt6PchdtqtbpR+2688UaxlKu79Ww2Gzp37uy0rs1mQ1ZWVqNkvCtt27Z1WtdisYgHkjabTRwR4+1vQa7eua7+Pyj0yR0vNm7c6PM+39f5uSwWCzZu3CieTLdr1w5//OMf8eGHH2LFihUoKytzWVIZqD/gE8pzT5kyBePGjWNJZZl5GytclZ2z2+2orq4W5wY3mUzo3Lmz04i548ePuz2pltLpdI32Sbt27RI7M8TGxjqNAJfOD56eno7ExERv37Ls/Ekuv/baa2I5ZZvNho0bN2LmzJlNrldRUYGzZ8/ixhtvBFD/GUyZMgXffPNNs6cCKC8vxxdffIHa2lqsXLmSx2hhLlTOLxrauXOnV8u5Kt3/9ddfo7q62mXHHFevEyrHOULSVUg4S283vO/uOX8vKCqVymbFTWnZbrVajZqaGmRkZCA+Pt7pOXd/arWa8+UFyJtvvomePXt6PU2VP9RqNd58801WRYhwoRovGvr5559b9PV27NiBXr16OSWUi4uLG1WkcnUhOS4urtFo1hMnTjhdIHe1nsPhQGZmplMn4rq6Ovz2228ul22oS5cu0Ol0YsK04YV6d6+p0Whw1VVXOT1+6tSpJq8pOBwO6PV6dOzY0enxH3/8EdXV1WJHJXcX2y+77DLo9XrxfnV1NQ4fPiwmihv+CcemSqUSQ4YMgU6nExPCJ06cwLFjx5yWc7WNVq1aYeTIkeJ6SqUSX3zxBUpKSpwSzq5e+7rrrsOAAQPEdW02G5YvX95oWVfuu+8+p8EkR48ebbJsMlCfhPS2U7occxE3d0DFxYsXQ+b4i+QXzHjREjmLmJgYTJ8+nd/hFuRr7qJdu3YYOHCgy+ccDoeYILfb7UhISHB6/g9/+AOuv/56l2XdpaPP27dv3+g7oNfrvape265dO6d1FQqFV9fDgPophKTT4J49e9bloMaGMjMzcf3114v3W/p46cCBA2H/m4nqJHdTvZ2a6i0lFSrlViZNmoTly5fj9OnTAXsNtVqN//znPxg+fHjAXoOcCeX6Ipm3peQbajhHty8a9n4VgmnDJHlCQoJToO7QoQPGjRsHm82G6upqnD9/Hunp6cjNzfUqeMnROxeo/y1G+vciVIRqvMjPz2/ywkVD/s7PVVZWhjNnzoiJ0tjYWDzwwAN44IEHkJ+fjx07dmDHjh3inJBqtRqdOnXCsGHD0L9/f87r1QL8jRVxcXEeR/PfcsstGD58OIxGY6M/oWS60WiEXq9v9Ppms1kcvWC1WlFWVubyOzt06FCnMttmsxmff/65U4l04U8ojy6MnpDj78svv/S5p+yiRYuwYcMGZGVlYdy4cVCpVF6d1NntduzevRt/+9vfAACzZs2SJcEtqK6uxjfffIN58+Zh0aJFsmyTvCNnrABC5/xCatu2bc1OyMXExHjVqcRqtWLnzp0YOXJks17P4XCgrq7O5fzSDeehdnVbGFndHAqFwq9qFsKxnru5p13NUS2933AUtdlsRkFBAdq1axeS369o06ZNGyxYsABPPfVUQEZrKBQKcdoACi3REC9c8fW8pbmEi9lC4lKY2sfbjo0N99tCNaSmWK1Wp3WFjvFNUSgU0Gq1SEhIgEqlgkKhQG1tLRwOh8vErfQxnU6Hq6++GkqlUkzgOhwOsaOSq3WEv65du2Lo0KHieiqVCjU1NSgtLUVSUpI4etnV648ePRpXXnmluG5hYSFWrVrl1f/vpEmTnDpx7d6926tY2aZNG7GzqODo0aNerZueno7s7GzxvsVi8fr30/BcS+gI1pTLL78cR48ebZG5iOUYUOFwOMJmnxINIiletETOok2bNpg4cSK/w0HQErmLyy+/3O91hWvswrmhu7nPW7du7fQ+WrVqhcGDB7tcR1oWvq6uTozfAmF6VIHw2g1HvSuVSvz3v/8VK1n5M61pc5SVlYX9byaqk9zx8fHIzMzEmTNnYLPZGs1tcfLkSQDN+wG1NJ1Oh6VLl2LixIkBmeciPj4e//nPf7wuEUIUThQKhTi3hicJCQliws5sNiM5ORnt2rXDVVdd5dU8OHL0zgXgVOqWAitU40VeXp7P+3p/5+cqLS3Fvn37XCaru3XrBoPBgLKyMvTq1Qt333231/MJNYdwoUAo/+drYlNYt+Gft9tyt7632/JmfXfbkq5bW1uL4uJiHDt2DBqNRtbkr7dtj4+PR3FxMd555x2nx0+cOIHq6mqn5FLDbQD1I5D37t0rvkeTyYT9+/e7/eyl5XA7d+7slFwWEnC+JJQOHDjg9bKC/Px85OfnAwD+/e9/4/PPP0dubi769evX5LqnTp0CUH8x79NPP/WY4O7Roweef/559OnTB5mZmeIImVdeeQWffvqpy3Wqq6uRm5uLsWPHYtCgQT6/N/JPqMYKOW3cuLFZybhly5ZBrVZj8+bNTS7rcDiwadMm/OEPf3CZkPYmWS38BWPuT4VC4TEB3fB+w+e8vXhO4W369OlYuXIljh07Jvu2u3TpggceeED27VLzRUO8CBWpqanQaDRiElaoXOQu4Ss8lpmZiX79+onJZpVKhaqqKpSXlzeZcB4wYAD69OkjvqbZbIbdbve4jvA3efJkpynrfvnlF2zduhUAnEYYC7eFf+Pi4hrNP2q3250SX67WB+r3FVdeeaXTuu3atYNWq0VqaqoYi1yt33BAgFqtdhrZ7e41XY2YTkhIQGZmpsd1gPpy3A21bt26yfWA+qnopJRKpVgOtan1G1ZVSUhIwBVXXNHka+r1enz//fctkrSQY0CFq3mfKXgiKV4EOmeh1+uxbNkyVg4kj4QOZd5WzE1JScHQoUPF+9JplYS/mpoa1NbWYt++fU6PV1ZWIiEhQZwqUDgvlU5NYrPZcOnSJezevdvpNVpSIDrbtrSoP2seNGgQPvroI+zbt6/RRcCvv/4aANyWUAhVgwYNwm233Yb169fLNioIqD9oHjt2LBPcRG4MGzYMmzdvbjI4NLd3LlB/MsbfYssKxXixY8cOnw5Gmjs/1+rVqxEbG+sy6XnkyBGxc0ZMTAzS0tJ8TgADviV9I+FATA5WqxWVlZXiSItQ0nCEuNBzteFcs1qt1mmkZFPzsAo9ZQE0KsldWFiIM2fOiCOrpXPDurottKu5NmzYgJycHHTt2hVHjx71uKzNZkNNTQ1mzpzZ5Al+hw4dkJiYiFWrVuHChQvilDG5ubmYNm2ay5LQQH2smTFjBvLy8nii34JCMVbIadeuXX6vO3LkSEyfPh2VlZVeJ94+++wz/Otf//L7NZtD2FdIk87uktWuRllrtVqW+Sav5OXl4aqrrkJRUZFs28zMzMTevXtl2x7JL9LjRSgQytZK98Vms9lpbtCGyWLhtlKpbDSq6brrroPD4WgyGSqMhhY4HA48//zzHtdxp2fPnujZs6fHZdy59dZbceutt/q17uTJk/2q/JGRkYFp06b59Zq9evVCr169/Fr35ptv9ms9tVqNe++91691s7Ozcdddd3m17IoVK5qV5Pb22o8cAyo4mCL0RFK8CFTOQqFQoG/fvmHz/0DBJQwWcZWorqmp8fi4N/N6u6LT6UL2ukwknLOG1pXQADIYDDAYDNDr9U69CidNmoSPPvoIL730Ej7++GPxYueXX36J3bt3Y/jw4Wjfvn2wmu23hQsXwmAwYPv27bIEjbi4OAwfPhyvv/66DK0jikwDBgyAXq9vkR66er0e/fv3D/jrRKNwihfCaFBvNXd+rnPnzok9hRsqKiqC0WgEUP9/6O+BH4UPb0ajSEdpuHvO1V9GRgY6duzoclSm9L5KpULXrl2dXttsNoslIaUH6xaLRbyoqVAokJ6ejv79+0OhUGDdunVeTTfhiXDCkpyc3OSyKpUKa9as8Wp+vq1bt4ojeARLlizB/v37MXfuXLdJbqD+d7l27VpMmTKlydch34RTrJCTv6Vmr7rqKmzevBk2mw1DhgzxuuR5TU2NX68nJCe8TUi7us+RTNRS1Go1Dh06hIEDB+LYsWPN6nilUCjQpUsX7N27N+Q6vkWraI0XrqSmpno9N7Ec0tLSGl24FSpn+EM6z6YvPM3zTNFh2rRpePrppwN+jtzcARUcTBFc0RIv5M5ZAPVJy++//x6zZs3CwoULG3WEp8gjDKBwl5xuKoHdUoNmhOmndDodYmJioNPpxGMRd4+/9957uHjxYou0D6g/Pgt3YX3Ws3r1auTl5QEADh8+DKC+XKQwvH/AgAGYOHEiACAnJwcvv/wynnjiCTz11FPiNm688UZMnDgRq1evxpAhQ3DzzTejqKgImzZtQmpqatBGDjSXUqnE6tWrMXfuXOTm5jarDIher8ftt9+O1157jUGCyINu3bohLS3N7yS3LycTaWlpnOPYB5EaL3wpYSPH/FyeXk+asNBoNB4TnnL+Ab9fOPL1NaXr+pKw9TVx62pb3q7rblvS9S0WCwoLC5GdnY3Y2Fif3n9zPqdgs9vtqK6uRkJCgtPj7du3x9GjR8X5wqWjdaR69OiBP/7xjwCANWvWeD03WHp6eqOEuFqtxsSJE8VS4k3p1KkTcnJy/L7QZbfbUVBQgL59+3pczmw2Iycnh0luL0VqrAi2tm3b4rvvvoNKpcIdd9zh8/QAbdu2dTma2tN9YQ5SonChVqvx/fffY/HixXj++edhs9n82saCBQswffr0ALSQpBgv/DN48GB8+OGHXi+/bt06pKWlITMzEwBwyy234MsvvwRQP9L43LlzTb4eUSiYMGEClixZEtC5iOXAwRTyY7xoTM6chVRpaSnWr18Pg8GA1atXM4cRJqxWq9ejqBv++XO87A+lUtkoGe1twrqpKVFdGTJkCI+XfBTWSe68vDy8//77To/t27cP+/btE+8LgcKTRYsWoUePHli1ahWWL1+O+Ph43HbbbXj22WfDukyLUqnEokWLMHbsWMyYMQNFRUVNlv+Uio2NRWZmJpYuXcr5HIm81BI9dGNjY/0uARatIjVe+HLQLsf8XJmZmZg9e7bLxOi6detw5swZAMDcuXP9HulAvhN6oWZlZfk9MiUcKZXKRgluAOjbt6+Y/BXKUBmNxkZ/0s4ew4YN82qqCAB4++23kZSUhG+//Rbnz59HZmYmJkyYgO7du2Pu3LniKHJP7e7evbvTnEveiIuLg06nQ3JyMm6//XbccsstWLduXZPrlZaWIj8/nx2jvBCpsSKYkpKScOjQIeh0OkybNs3tPPLuaDQa/PnPfw5Q64hCz9SpUzFgwAAsXboUW7ZscZrKwxWFQgGNRoPRo0fjlVdecTlXLcmP8cI/Y8aMwfr1670ePTVmzBinigTZ2dnIzs4GUD+di6eLtgqFwutS0kSBFui5iJvi7YAKDqaQH+OFa0LOorCwEF988YVso2qrq6uxfft2zJ07F4sWLZJlm9Q0u90uzjvtbaJaeNzdoAS5KRSKJpPT7hLVLT0NFY+XfKcoLy/nhJZRoKamBmvXrkVOTg5KS0thMBjcjsiLi4tDq1atMGvWLIwfPz5k5wuIBmaz2a95kKjlNPyMampqMGDAgID20O3YsSPnWCUA9WWrvE3M6fV63HDDDY0ebzg/16FDh9xu484778R7773n8rn33nsPhYWFUCgUeOKJJziCrgUxVjRffn4+br31Vq8uOt17772YMmUKevbsCb1eD6PRiP3792Px4sXIzc1tcv1WrVrhb3/7G15++WWfTubfeustcXSezWbDxo0bMW3aNJSXl3tcT6lUYuHChZg0aZLXr0XkTvfu3b0uNatWq3H27FlkZmbif//3fzF//nyfXy8rK8ur6gjkHcaL0NfwM5o5cyZ++uknnDp1ChaLBQ6HA2q1Gnq9HoMHD8Zdd92FkSNHBrvZRF7LyMhosvOGHLRaLYqLiwP+OpGK8SIwZs2aJftcxHKJjY3FSy+9xApQ1GJ2796NSZMm+dzxY968eXjppZdw6NAh9OzZ0+Uyer0eq1ev5qA9HzgcDnFqOF8S1VVVVSgpKUFSUlKLTJUjVO/yZhR1wwR2OF2n5PGSb8J6JDd5T6fTYcqUKZgyZQry8/Oxb98+5OTkoKioCGq1GpmZmUhLS0NMTAzatm2LP/3pT7juuuuC3WyisBPoHrp6vR7Lli1jgpsA1PfI3rx5s1eJskDPzyVUCgm3A0cioH66Cb1e79V+e926dV6NoHZHr9fjyJEjPvdWX7RoETZs2ICsrCyMGzcOKpVKnI/NE7vdjh07djDJTbLwpdTs999/jzZt2qCoqAht2rTBsmXLnJ5/6KGHvHo9omhVV1cnjsSw2WziPPG33norrrnmmiC3jsg/o0aNwqZNmwL+OqNHjw74axD5KhBzEcslMzMT48ePD3YzKErU1NRg5syZPl83zc7Oxrx582AymTwuZzAYMGPGjKgbIORwOGCxWNwmpz0lsP2dp1o6daG3NBqN34lq4Xg40vF4yTdMckehbt26oVu3bjAYDCgsLETbtm0xa9YsHDhwAFu3bgWAFul5QxSpBg0ahNtuu032HrpxcXEYPXo0Bg4cKNs2KbwNGDAAer3e73ngfZGWluZxfi6hRD/LlFO4aonpJtRqNaZNm4ZVq1b5vG5+fj7y8/MB1M/j9vnnnyM3Nxf9+vVrct1Tp075/HpErvhSOk0onZiZmYkHH3yw0fNNJbkjpXQakb+klTqSk5PFC7otNf8gUSC89tpryM3N9euiuLfUajVeeeWVgG2fyF+Bmou4uTiYglramjVrvK4OJfXqq69i3759UKlUaNWqlcdli4qKsHbt2rCsTlBXV+d1ue+Gy7ir3Cs3lUolJp11Oh3atGmDxMTEJhPVOp2OeScv8HjJN/xGRTHh4pQwp6v0R6PRaILSJqJIIXcP3bi4OAwfPhyvv/66DK2jSNGtWzekpaU1K8nt7fxcqampbufnEsoaAWA5OwpbEyZMwJIlSwI63UR2djbGjx+P//u//2v2tjZs2ICcnBx07doVR48e9bgsEyIkl5EjR0Kj0XhVOi01NbVZr6XRaFiGmaJaWVmZeDs1NZVJbooIaWlp+N///V8899xzss3BKqVQKPDSSy9xfnoKWcJcxGPHjsWMGTNQVFQknksHAwdTUDDk5OT43Ll88ODBuOeee9C7d28sXry4yeXNZjNycnKCluS22WxeJ6obPh7IxKaUME+1p1HU7hLVGo0GCoWC01sESFpaGhYsWICnnnqKx0teYJI7igk9e4SysnV1deJzTHITNY+cPXT1ej1uv/12vPbaa2KnFCJBS4w+1Wq1+Nvf/ub2+bq6OjGmcCQ3hatATzeRkpKCN954AzqdTpZ9uTDSIjk5ucllo6WkF7UMlk4jahnSJHfHjh1x+eWXQ6VSoV27dkFsFVHzTZ06FStWrMCZM2dk33aXLl3wwAMPyL5dIrkNGjQIeXl5WLt2LXJyclBaWgqDweByFKZCoYBer4der4dGo8HJkyc5mILCVn5+PkpLS31aR6lUYvHixVixYgUOHTrk9XqlpaXIz893O2CjKXa73eVoaW8S1S0xn7JAKOXtzShq6eMxMTGcbjCETZ8+HStXrsSxY8dk33akHS8xyR3FhF4gTHITBUZze+jGxsYiMzMTS5cuxaBBgwLYUgpnLTH6ND09HePGjXP7vFarxWOPPYba2toWK41EFAiBnG5i2LBhYsn/Tp064eDBg16tm56ejpKSEqfH1Go1Jk6ciOrqahw+fLjJbQhlo4nkwNJpRC1DWq68Y8eOaNu2bfAaQySzDz74AHfeeScuXrwo2zYzMzOxd+9e2bZHFGg6nQ5TpkzBlClTkJ+fj3379mHHjh04deoUbDYbKisrodPp0KlTJzz66KPo27cv7HY75s6di82bN/ucKJTiYAoKlry8PJ87lU+fPh0dOnTATTfd5NN6BoMBeXl56NixY6MktDeJ6tra2oCMonVFo9H4laiOjY3lbziC5eXl4aqrrkJRUZFs24zE4yUmuaOYp3LlnBuBSD4Ne+gWFhaisrLS5bJKpVLsoTt16lSMHz+e8yKRR4EefZqWlobnnnuuye+hWq1m7KCIEIjpJoYMGYInn3xSfGzYsGHYvHmzVyfMb7/9NpKSkvDtt9/i/PnzyMzMxIQJE9C9e3fMnTsXVVVVHtdXKpVeT0tA5A2WTiNqGQ3LlRNFErVajf3792P48OE4duxYs+KJQqFAly5dsHfvXp6PUNjq1q0bunXrhkmTJomPffPNN2IiIiUlBcDvgynuuOMOzJw5E5cuXfKpqhsHU1Cw7dixw6d9flpaGl588UXMnz/f56n67HY7li9f7tf83/4Q5qcWRkn7kqhm/CJX1Go1Dh06hIEDB/J4yYPIejfkE2G0nZDklo7kjrQvOlGwSXvoLlq0CHl5eTh9+rRYNUGlUqFTp07iSD9/S+lQdArk6NNRo0bh2muvlW2bRKEuENNNvPTSSzh//rz4+IABA6DX6706SV+3bh2mTJmCBx98EHq9HkajEfv378cTTzyB3Nxcr9ogjCAnkgtLpxEFnpDkjomJQVxcXJBbQyQ/tVqN77//HsuXL8czzzzjV4UQtVqNBQsWYPr06QFoIVFwpaeni7eLi4vRpUsX8f6AAQPwwQcfYO/evXj33Xc9ljvnYAoKJadOnfJp+QULFqC0tNSrebhdkXYa9IZCofB6XuqGj6vVapb/JtnJcbykUqnw/PPP4+GHHw5AC4OPmcwo1jDJLf2BsFw5UeBYrVZUVVUhOzsbTz31FPr27RvsJlEECMTo0+HDh+Pll192Ss4RRQO5p5touG63bt2QlpbmdZJ73bp1Pr8HQVpaGjtOUUCwdBpR4AhlaoH60XsOhwNVVVWw2WxQq9WIj48PcguJ5DN9+nSMGzcOjz32GHJzc1FXV+dxpJJCoYBGo8Ho0aPxyiuvsPIHRayMjAzxdsPpi4D6c45JkybhgQcecFnunIMpKBT5MsVd586dMW3aNMyePRtZWVni47GxsdBoNOjQoQMqKys9JrJVKhU6duzocRS1NFGt1WqZqKaQ5O/x0qhRozBjxgxcddVVLdjalsUkdxQbN24czp8/j/bt2wNguXKillJaWgqHwwGHw8F5U0g2gRh9+tprr8FisTS5/Pnz53H8+HHExMSgc+fOaNWqld+vTRRKGk43IecIiWnTpuHpp5/2qbygr2JjYzFt2rSAbZ+iG0unEQVOZWWlGGtSU1NRUlKCd999FwDQq1cv3HLLLcFsHpHs0tLSsHLlSgDAtm3bsGnTJnz55ZeorKyEw+GAWq2GXq/H4MGDcdddd2HkyJFBbjFR4KWlpUGlUsFms7lMcku5KndOFIp8uQ6anZ0NlUqFxYsXuxzJffr0aSxatAhz5sxxu4309HTcd999frWVKNS4Ol764osvYDQa4XA4oNFokJaW5nS8ZDabUVBQEOSWBxavIESxdu3aAYDYE0parpwjuYkCw263o7y8HACg1WqRkJAQ3AZRRJF79Km3Lly4II68S0xMZJKbIop0ugnpCImTJ0+iqKgICoUCmZmZ+Otf/+rTCIkJEyZgyZIlOH36dMDanpmZifHjxwds+0QsNUsUGML5AlCf5JZ2/rDZbEFoEVHLGTlyJEaOHImdO3ciLy8PADB+/Hh06NAhyC0jallKpRKtWrXCxYsXUVpaCqvVys6AFPY6deqEgwcPerXsoUOHcOeddzZ6fMGCBUhMTMSsWbNw4sSJJl+PKBIJx0uffvopfvnlFwD1gyn0en2QW9byGBlJxCQ3UeCZTCZxZGxMTAwSExOD3CKKRIEcfeqKNJEeGxvb7PYThaqGIyQWL14Mk8mEhIQEn0dN6HQ6LF26FBMnTmxW5QV39Ho9li1bxvn2qEVMnz4dd9xxBx555BHs3LkTVquVpWaJmkGa5E5JSYFKpRLvM8lNRBQ90tPTcfHiRdjtdly6dAmZmZlerWe325Gbm4vKykokJSXhjjvuCHBLibwzbNgwbN682asqUAaDAZ988kmjx2fPng0ALp+TUiqVGDZsmF/tJKLwwSQ3iaRJbvYMJAoMk8kklqbVarVMclPAuBt9+uuvv6K8vBxKpRI9evTAnXfe2ez5uaTllmNiYuRoPlFYiIuLg8lkQk1NDRwOh89zdw0aNAi33XYb1q9fj+rqalnbNXr0aAwcOFC2bRI1JTU1FX//+9/Rrl077Ny5E5s2bcLOnTthMBjgcDigUqnQqlUrlpol8gKT3ETOmjMlBlE4azgvt7dJbqVSiVOnTqGmpgbJycmBah6RzwYMGAC9Xo9Lly4F/LX0ej369+8f8NchouBiJjNK2Ww2HDt2DEVFRVCr1ejUqZNYXlClUnGeYKIAaTiSOz4+PsgtomggHX168OBBbNmyBUB9aZvevXs3e/scyU3RKi4uDkD9cVVtba1f3/+FCxfCYDBg+/btsiS64+LiMHz4cLz++uvN3haRv0aOHIm+ffti2bJl+O9//4uMjAyMGjUKd911V7CbRhQWGpYrlya5/ZkWgCgc+dp5kCgSpaeni7ebmpe7ocTERNTU1MBkMvnVIZcoELp164a0tLRmJbm9HZ2dlpbWrAEdROEmWjsFMpMZperq6vDxxx/jiy++wJ49ewD8frLMUuVEgSMdyZ2cnMwOJdTitFqteFvocNFcTHJTtBKS3ABQU1Pj1zaUSiVWr16NcePGNXvuJL1ej3vvvRerV69mfKGgE0ab2mw2OBwO2WIOUTQQktwqlQqJiYmck5uIKEpJk9zFxcU+rZuUlASgPm6YTCZZ20XUHNOmTQt4FcDY2FhMmzYtoK9BFArYgYlJ7qglnZdV+CEI5cpZqpwocMrLy8XfWnOTGUT+YJKbSD7SJHdzRmErlUosWrQIq1evRseOHX3+HcXGxqJjx45YvXo1Fi5cyAQ3hQSbzSaOPlUoFExyE3nJ4XCISe6UlBQolUqO5CYiilIJCQniOYc/I7kFRqNR1nYRNceECRPQpk2bgL5GZmYmxo8fH9DXIKLQwCtgUUpaukC4EMqR3ESBJz0padWqVRBbQtFKuo8XOlw0l1CdQK1Ws6MURRWdTifelqPU+KBBg5CXl4eXXnoJ3bp1Q3p6utteuUqlEunp6bjiiivw0ksvIS8vD4MGDWp2G4jkYrPZoFAoxD+5Yg5RpKuqqhLPzVNTUwHAqfOStMM6ERFFNoVCIV47MplMPp1zCCO5AaCyslL2thH5S6fTYenSpQEb/KPX67Fs2TKn83WiaBCt5cp5JTpKeRrJzSQ3UeAYDAbxNkdyUzAEciQ3R3FTtJFrJLeUTqfDlClTMGXKFOTn52PlypX45ptvUF5ejpSUFMTGxqJTp04YNmwY+vfvzznGKGQJJZVVKhVHchP5QDraLiUlBUD9ObtarYbVauVIbiKiKJORkYGzZ88CqC9Z3rFjR6/W40huCmWDBg3CbbfdhvXr18t2Lg3Un6OPHj0aAwcOlG2bRKGM5cqZ5I5aDZPcDoeDSW6iFqBUKtGzZ0/YbDb07t072M2hKBSIJLcwkjvQcyoRhRo55uT2pFu3bhg8eLA4km/atGnsIEVhQzjfUKlUUCqVHMlN5CXpaDth/w/U/5asVivn5KaoFK0jk4gA53m5L1265HWSWzqSm0luCkULFy6EwWDA9u3bZUl0x8XFYfjw4Xj99ddlaB0RhQsmuaNUw3Ll0t7gLDVLFDg1NTVITk5GamoqsrOzg90cikJylyt3OBxo27YtzGYzkpOTm709onAid7lyV6S/U2knFaJQJ01ycyQ3kfcqKirE29Ik94QJE6BUKtkpnaIGRyYR1ZMmuYuLi71ej+XKKdQplUqsXr0ac+fORW5urlP1S1/p9XrcfvvteO2115ymeSGKJtHaKZDZzCjVMMktvYDKJDdRYNTV1YllnRMSEoLcGopWco/kVigUGDduXLO3QxSOAlGuvCGhUgLAJDeFF2m5cuF8w+FwMGlB1ARX5coBoHXr1kFoDVFoiNaLtkSAc5K7pKTE6/Wk5cqZ5KZQpVQqsWjRIowdOxYzZsxAUVGReO3UG7GxscjMzMTSpUsxaNCgALaUKDTx/JpJ7qglLVfecCQ3e4YTBYbJZBJvS082iFqSRqNpNE0FEfmnJZLcQmcUhULBJDeFFSHJrVQqneIOv8dEngkjuRUKBavkUFTjRVuielqtFqmpqSgrK0NJSYnXnT7UajX69euH2NhYTnlEIW/QoEHIy8vD2rVrkZOTg9LSUhgMBqcchkCpVEKv10Ov12Pq1KkYP368U5U1IoouTHJHqYZzcrNcOVHgGY1GGAwGKBQKJhcpaBQKBTQaDSwWC0vHEjWTTqcTk3eBmJMb+H0kt1ar5cVeCivSkdzCd9disTDJTeSBw+FAZWUlYmNjkZSUxHNzIiICUD+au6ysDHV1dSgvL/c6oTd8+PAAt4xIPjqdDlOmTMGUKVOQn5+PXbt2Ydu2bTh27Biqq6uhVCrRq1cv3Hrrrejfvz+6desW7CYTUQjgGVMUU6vVUCgUjcqVcyQ3UWAYjUacPHkStbW1sNvtuOeee4LdJIpSWq2WSW4iGSiVSuh0OlRXVwe8XDkTgxRu2rVrhzFjxiApKQkXL14EAHbyI2pCTU0NLBYLYmNjnUqVA8CJEydgNBrhcDjQu3fv4DSQKEhYrpyiXXp6Oo4ePQqgfl7uDh06BLlFRIHVrVs3dOjQAUOGDMHWrVuxf/9+WK1W/P3vf+f3n8iNaD1eYpI7SqWnp2POnDkoKChAu3btcOnSJfE5JrmJAsNoNIpJxdTU1CC3hqKZsJ+XI9lw/PhxbN++HbGxsbj++utxxRVXNHubROEk0Elu4XcaExMTkO0TBUpycjKSk5Nx8uRJcR5Idq4i8kwoVQ40Pl/Ys2cPzp8/DwDo1asXq3tQxON3nOh3GRkZ4u2SkhIm+SiqlJSUiLmLqqqqILeGKLTweIlJbvr/WK6cKPAMBoPYo4rzIVEwCSNC5Ug2mEwmGAwGAIDZbG729ojCjVAq0GKxwGq1ynoc5XA4xJHcTHJTuJJWIWCSm8iz8vJy8XbDJLdSqRRv22w2nrcTEUWR9PR08XZJSYlP65rNZhiNRsTGxiIxMVHuphEFXEJCgnhbeqxERAQwyU3/n3Q0H0+WiQJDWjGBSW4KJmEkt9Vqhc1mg0ql8ntb0sR2bGxss9tGFG7i4+PF29XV1UhKSpJ1+1OnTkVtba1TcoMonEirRLFcOZFnZWVl4u2GSW7peTqT3BRtorX8JpEgNTUVarUaVqsVxcXFXq935MgRfPzxxwDq5+fu169fgFpIFDjSzhlChSgiaixaj5d4VkQAnEdys1w5UWBIk9zSUlNELa3hqDphJKo/hFGmAJPcFJ2kvx+5k9wKhQKtWrWSbXtELam8vByVlZWoqKgQqxxwJDeRZ9Jy5Q3n5JZ2SrRarazwQRGP5TeJfqdUKpGeno7CwkKUlZV53XFQOgKWyUEKV9Ikt/RYiYh4vAQwyR21SktLsWfPHhgMBlx33XWw2+3ic0xyEwVGaWkpgPpRGA0vWhG1JDmT3NKR3LzYStEoLi5OvF1TUxPElhCFlkOHDmHXrl0oLCxETEwM0tLSmOQmaoJ0JHfD84WGI7mJiCi6tGrVCoWFhXA4HOKUYU2RJgeNRmOgmkYUUMnJyeJtdtYgooZY9zBKVVZW4sCBA/j1119RWFjIObmJAszhcIjzxmi1Ws6DREElZ+lYaZK7OclyonAlTXJXV1cHsSVEoUVIwqlUKrF3OcuVE3kmnC/Ex8c36jzYcE5uomgSreU3iaSkFQG9nZc7MTFRPA5jcpDClfQaqslkCmJLiEJbtB4vMckdpaRfeIVC4XTBiSO5ieRnNpvFEX5MclOwNRzJ3RzScuUcyU3RqGG5cjlVVFTgwIEDOHLkiFgNhChcCOcbKpVKTM5xJDeRexaLBVVVVQAaj+IGnMuVM8lN0YDlN4mcSZPc0unwPFGpVIiPjwfAkdwUvlJTU8Xb/B4TOePxEpPcUUtanlypVDLJTRRgRqNRTAbGxMQwyU1BJWeSm+XKKdoFciR3UVERtm7dio8//hj5+fmybpso0FyN5GaSm8g9YRQ34DrJLa24Jq3ERkRE0aFVq1bibW9HcgO/l3o2mUzsJEVhSVqunCO5iaghJrmjlDTJrVAonE6SpT3EiUgeJpMJSqUSKpUKOp0OsbGxwW4SRbFAlCuPiYlxKqNJFC0CmeRmpQQKZ8JFVKVSyXLlRF7wNB83wJHcFN2itfwmkVRCQoI4KtuXJDfn5aZwl5iYKF5vEqreEBEJOPlylJKeICiVSqdRFRzJTSQ/o9GIHj16AABGjBjBUiIUVIEoV84EHEUrablyYVoKuUh/n9LfLVE4kI7kFs49OJKbyL2mRnLHxsYiISHBqToCUSTj95yosfT0dFRVVaG6utrrDrZJSUni7crKSpcxhiiUxcTEoHXr1gCArKysILeGKHRFa6dAJrmjVMNy5dKR3ExyE8lPWk6HJxQUbHKO5L7xxhtRXV3tVEKTKJoEciS3NCHIjiQUbqRJbuHcgyO5idxraiT34MGDMXjw4BZsERERhZr09HScPn0aAFBaWurVOtKR3JWVlYFoFlFAqdVqdO7cGQ6Hg0luogbYKTACypX/+OOPGDt2LNq3b4+srCzcdNNN2LRpk9frr1mzBikpKW7/du3aFcDWB0/DcuWck5sosKQloTgfd3AwXvxOmixr7qi6a665BgMGDEDfvn2b2yyisKTRaMRR1oFMcvP4rGUwVshHON+QjjplkpvIvaaS3BRaGC9aVrSOTCJqKD09XbztbZJbOpKb8xm3PMaL5lMoFOI5t3RKLyIiIMxHcn/77be4++67ERsbizFjxiAhIQGbN2/G5MmTce7cOTz88MNeb+vWW29Fz549Gz3evn17OZscMhqWK5eO5OZoPCL5SU8kEhISgtiS6MR44UzOcuVEVF+y3GKxyF6unHNytyzGCnlJk9zCbcYcIveEcuUajcZpKgwKPYwXLYMjk4gay8jIEG9LO0d50qFDB0yZMgWJiYmIjY0NVNPIBcYL+Wg0GtTW1vJ8gsiDaO0UGLbZTKvVilmzZkGpVGLLli24+uqrAQCPP/44RowYgfnz5+OOO+7wekc/atQoTJgwIZBNDikcyU3Usk6dOoXDhw8jJiYGFRUVHM3dghgvGpOzXDkRAfHx8aioqEBNTQ3sdjuUSnmKJTHJ3XIYK+QnlCtXKpXib4Ixh8g1m82GiooKAEBycjKTeyGM8YKIgqlVq1ZijDAYDF6to9Pp2HkqCBgv5BUTEwOj0Yjq6mpYLBanwRtE0YznDWFcrvzbb7/FqVOncM8994hBAqg/IZw7dy4sFgvef//9ILYwtCUmJqJz585o3749kpOTmeQmCrCioiKUlpbCYDCwPFQLY7xoTK6R3BaLBaWlpaiqqhKTGUTRSLho5HA4ZB3NLf198iQ+sBgr5HfXXXfhf/7nfzBnzhzEx8cD4EhuIncqKirEkRfSsrJSp0+fxscff4yPPvoIp06dasnmkQTjRXBE68gkooY0Gg1SU1MB1I/klg5iotDCeCEvg8GAvLw8fPPNNzh06FCwm0NEISRsR3Lv3r0bADB8+PBGz40YMQIAsGfPHq+3d/DgQZSWlsJms6F9+/YYOnQo0tLS5GlsCOrYsSMyMzNRUFCAdu3a4fvvvwdQ3/NDrtFHRFTPbrejsrISQH3PQ47iblmMF43JleQuKCjAhx9+CAC44YYbMHjw4Ga3jSgcSUdG1NTUiAm95pL+PjmSO7AYK+QnjOAW5q23WCxMchO5IZQqB+D2XKGiogJHjhwBAFx22WUt0SxygfGi5XBkEpFrGRkZKC4uhs1mQ3l5OeLi4oLdJHKB8UJeMTExYqcO4RorETmL1k6BYZvkPnHiBADg8ssvb/Rc69atkZCQgJMnT3q9vbffftvpvk6nwxNPPIHZs2d7tb7ZbPb6tUKFcJFJmEPSarVCq9U6lcak4JJ+RhSavPmMhHI6drsdKpUKWq026PuMaJqHifGiMbvdDqvVCgCoqqry2CZP3/HKykpxO0qlMiTeWzRirAg+jUYj/hbKysqQkJDg9Ly/n5FSqURMTAysViusVmtQKiZES7wItVgBhEa88IWn77lCoYDVakV1dXXYva9IwngRuoqKisT9fHJyssvPSHr8VlNTE1K/pWiJFQDjhRy83RdZLBbxO19bWxt27zOcMV6EtqSkJPG84MKFC14lOk+fPo2LFy/CaDRiyJAhQaviyXhRj/HCO9J9kU6nE5PcxcXFYfdeIhXjRfDV1dV5PF4K18/Il3gRtkluoceOu1JeiYmJXvXq6dChA/71r39hxIgRyMrKQllZGb799lu8+OKLeP7556HT6fDAAw80uZ0LFy6EbanWixcvoqSkBEajETqdDgUFBcFuEjVw8eLFYDeBmuDpMyouLkZFRQVqa2thtVpRWlrqNFqjpalUqqga/cF40ZjFYhHf88WLF73a77v6jp85c0bcTnl5OeNHkDFWBI/RaBR/C54uVPj6GfXp0wd9+vQBAJw7d87/BvopmuJFqMUKIDTihT9cfc+rqqpQWVkJk8nEWBECGC9Cz7Fjx5z2Q64+o+LiYnGZCxcuID09vUXb6E40xQqA8UJOTe2LLl68KP5fFhYWcrRqEDBehCabzYaqqioA9fEjOTm5yXV27twpJl1bt26NlJSUQDbRJcYLZ4wX3rt48SLq6urEgXlnzpzhOUWIYbwInpKSEqdzBHfTWITTZ+RrvAjbJLdcbrjhBtxwww3ifZ1Ohz/96U+45pprMGzYMPzzn//ElClToFZ7/q/KysoKdFNlZ7FYcPHiRbH3mEKhQHJyMtq1axfsptH/J/2MOBdnaPLmMzKbzVCpVIiJiUGbNm3QoUOHFm4lySGS4oXdbhdPtBITEz3u9z19x8+dOydup0OHDowfQcJYEXzl5eX47bffAMDlsRQ/o+ghV6wAQiNe+KLh9/zHH39EdXU1VCoVMjIyxJ7jWVlZUKlUQW5tdOK+KHRptVpxZF5SUpLLz8hisYjHXXq9nsddYY7xoul90cWLF8XvfGZmJr/zLYjxIrQlJCRg7969qKqqgs1m8+q30alTJ5SUlACoT7ry9xQ+GC/q90Xt27cXp/BSqVT8DocIxovgO378uHi81KZNG7Rt29bp+Wj4jMI2yS18cO56PBmNxmb1SuvevTv69++PnTt3Ij8/H1deeaXH5cOt3MqBAwewa9culJeX4+677wYAqNVq6HS6sHsv0UCr1fJzCXGePiNhOgClUon09HR+li2M8cI1nU6Huro6OBwOr9rk6jvucDjEE6mkpKSQeW/RirEieFJSUsTfgtVqdfs58DMKXaEWK4DQiRe+Er7nR44cQXFxMTQaDdq3by/+RpRKZdi+t0jBfVHoMZlMUKvVUKvViI+Pd/kZxcXF8XcUAhgv5NPUvkir1Yrf+ZiYmLB9n+GM8SI0ZWZmIjY2FlVVVSgvL/fqM9Lr9eLvqba2lp9rC2C8kI9Wq0ViYiJiYmJQV1eH6urqsH0vkYrxInhiYmK8Ol6K5M9IGewG+EuYz0IotSJ18eJFmEymZpdA0ev1AIDq6upmbScUmc1mlJeXi/Op1tXVAUDQ5mQhimSXLl0Sb3szVxLJi/HCNaH3XnPmZJHO8xKpB0pE3tDpdOLtmpqaILaE/MVYIT+hFKJKpXLqMR5uc4ERBZrD4RCnMkpOToZCoXC5nFL5++Ubd2UIKfAYL4LD4XAEuwlEIUOhUKBVq1YA6itKeXNsJS2ZbTQaA9Y2+h3jhby0Wq14TmEymRgXiEgUtknuQYMGAQC2b9/e6Lmvv/7aaRl/2Gw2/PTTTwAQkeUvpCfFDodDvM/SgUTyE0pCARBPRKjlMF64JnRqEjo5+UOYDwlgkpuiW3x8vHhbrgsMtbW1WL9+PT755BP88MMPsmyT3GOskJ+Q5FYqlU4daZsTd4gikdDxHABSU1PdLictQyosTy2P8aLluOvwQURAeno6gPprutKBFe4kJiaKt72ZB5qaj/FCXtIktzCam4gICOMk95AhQ9CxY0ds2LABBw8eFB+vqKjA66+/Dq1Wiz/96U/i40VFRTh69CgqKiqctnPgwIFG27bZbHj++edx8uRJDB48GJmZmQF7H8Ei7e0kTXhzJDeR/AwGg3hbOBGhlsN44ZocI7mlI1aZ5KZoJh3JLdfJttlsxvHjx3H48GEUFBTIsk1yj7FCfsI5hlKp5EhuIg+EUdxA/Uhud6Qd0oVOJNTyGC+IKBRIB1AUFxc3uTxHcrc8xgt5SZPcNpsNVVVVQW4RUeiJ1goHYTsnt1qtxptvvom7774bo0aNwpgxY5CQkIDNmzejoKAA8+fPR4cOHcTlX3jhBbz//vtYunQpJkyYID4+dOhQXHnllbjyyiuRlZWFsrIy7NmzB8ePH0d2djYWL14cjLcXcNLENpPcRIHlcDjQtm1bWK1WtG/fPtjNiTqMF65JR3Lb7XanEpjeEkZyNxylRxRtYmJioFKpYLPZZCtXLk0EShOEFBiMFfKTVopikpvIvbKyMvG2p7k5OZI7NDBeBEe0XrQlckea5JZWD3QnLi4OarUaVquVI7lbCOOFvDQaDZPcRC6w8k0YJ7kB4MYbb8S2bdvwj3/8A5s2bUJdXR169OiBF154AWPGjPFqGzNnzsQPP/yAnTt3oqysDFqtFp06dcL//M//YObMmR5PMsOZNLEt7QUuPXEmInkolUp07NgRqampzZ5vh/zDeNGYNOFQV1eHmJgYn7chzMkdGxvLgyqKagqFAjqdDiaTSdZy5QJ/fp/kO8YKeUnn5Ga5ciL3pCO5PZUrj42NxVVXXQW1Wo3s7OwWaBm5w3jRMnh+QeSer0luhUKBxMRElJWVcSR3C2K8kE9MTAzS09ORnJyMIUOGoG3btsFuEhGFiLDPaPbp0wcbNmxocrm33noLb731VqPHFyxYEIhmhTxpL1hpkpsj8YjkVVdXJyYCExISgtya6MZ44azhqDp/kmgTJ06E2WzmaCIi1I+OEJLcDoej2RdmpYlAjuRuOYwV8pEmuTmSm8i9huXK3Y1MSkhIwOjRo1uoVdQUxgsiCqb4+HhxyqSSkhKvzj+EJLfZbEZtbS070rYQxgt5aDQaxMXFiX/MYRA1Fq2Vb8I+yU3+4ZzcRC3DZDKJtxMTE4PYEiJncoyqi4+PR3x8vFxNIgprwkUmm83md8cRKY7kpnAnJLkbTmnBkdxEzoRy5QqFwmOSmyiaRetFWyJP9Ho9KisrUV1dDZPJ1OQ1p8zMTNjtdiQlJcFqtfIcg8IKO80SucbKN0xyRy3OyU3UMoxGIywWCzQaDUdyU0jhCQKRvKQdPqqrq2VNcnMkN4Ubh8PBkdxEXnA4HGKSOzk5GSqVKsgtIgodvGhL5FlaWpo4v/alS5eaTHKPGDGiJZpFFBDS82ueTxCRlDLYDaDgcDcnN0+qieRVWVmJH374AXv27EFeXl6wm0MkYsKBSF7CSG4AsszLLf1dMslN4cbhcKB9+/Zo27YtMjIyGHOI3KipqRE7NUXLnJpERCSP1NRU8XZxcXEQW0IUeMLAvLKyMpw4cQKHDh0KcouIQk+0Vr7hSO4odcUVVyAhIQFFRUWIjY0VH+dIbiJ5GQwGsVOJNAFCFGzNLR1rNBrx66+/IjY2Fq1bt0abNm3kbB5R2ImLixNv19TUNHt7LFdO4UypVGLChAni/bNnz4q3Wa6c6HfCKG7AOVnhisPhwBtvvAGr1YrWrVvjz3/+c6CbRxQyovWiLZEner1evM0kN0U6odNsfn4+zp8/j5iYGFx11VVBbhVR8LHyDZPcUatdu3ZIT09HQUEBjEaj+DiT3ETyunTpkni7VatWQWwJkbPmjqorLS3Fjh07AAD9+/dnkpuinjTJLcd8qhzJTZGEI7mJXPMlya1QKGCxWGCz2WC1WgPdNKKg40VbIs9SUlKgVNYXaZVee/KGw+Hgb4zCinA+odVqYbPZZDnnJqLIwCQ3OY2mUKv5lSCSk/REIz09PYgtIXLW3IQDR5kSOZNW65BjJHdWVhZ69+6N2traJufXIwp1THITuVZeXi7e9qZcuVqtZpKbohJHchM1plarkZKSgsrKSly6dAl2u11MertSV1eHDz74AEajEa1atcK4ceNasLVEzaNUKqHRaKDRaGC1WmGxWFBbW8vrUUTEJDfB6QSZI7mJ5MUkN4Uq6f7en4SDNInHUvxE8pcrv+KKK3DFFVc0eztEoaC5U2QQRSpfRnIDgEqlAgDYbLaAtYkoVHCUKVHT0tPTUVlZCavVitLSUo8VBNVqNS5evIi6ujoOcqKwpNVqodVqYTabAdRXUGOSm4jcd++iiGY0GlFWVobKykoxMABMchPJTbhwpVarvbpwRdRSOJKbSF5ylysnCmeVlZV49913sWrVKnz77bccyU3khjTJ7c1IbmGEHkdyExER4DyYoqSkxOOyCoUCSUlJAOqvC7NCAoUbjUYDrVYLu90OAE5TsBJR9GK3rSi1fft2HDx4EJWVlRg4cKD4OHvyEcnH4XCgoqICQH1CkeVmKZQ0d1SdtINUbGysLG0iCmdylysnCmdWqxUXL14EAKSlpTW7eghRpBLOFRISEpxGJrkjnK8LF3eJogWTcUSuSUduFxcXo3v37h6XT0xMhMFggMVigdlsZlU2CitarRYajUasaMPO5UQEcCQ3wfkEmUluIvmYzWYx0aHVapGQkBDkFhH9jiO5ieQld7lyXsylcCYtpaxSqcQ59ACWKycS1NbWwmQyAfBuFDfwe7lyjuSmaMBy5URN82UkNwBxJDfAUbAUfrRaLWJiYmC32+FwOMTjKCKqF63XkZjRjFLSxLb0IhTLlRPJx2g0iolAnU7nlAAhCrbmJrk5kpvImVKphE6nQ01NjSw9yt9++21UVVUhKSkJU6dOlaGFRC2nYZIbqI87dXV1HMlN9P+Vl5eLt72d1kjolM45uYmICACSk5Oh1WphsVhQXFzc5PLSCoOVlZXIyMgIZPOIZCWM5Abqj4U4kpuInQIBjuSOWkxyEwWeyWQSL+QmJycz6FBIkbNcOUdyE9UTyv3JMZLbYrHAYrFw1CuFJen5hTCHMEdyEzmTJrn9GckdrSM1KDrx+07kmkKhEEdzV1RUOFVcc4UjuSmcabVaccCGzWbjSG4iAsAkd9SSJrmlpc5YrpxIPkajEb169cLVV1+NYcOGBbs5RE44kptIfkLFjtra2maXkhUuULETCYUjdyO5Ac7JTSQoKysTb3s7klvoNAJwXm4iIqrnS8lyaZK7srIyYG0iCgQhya1UKpGYmMg55YkaiNZOgcxoRinpF54juYkCw2QyITY2FrGxsejatWuwm0PkpLlJ7uTkZNTU1MBqtbKDFNH/J52Worq62ukiki9sNpuYJJf+VonChTT5JiS5pSO57Xa7U7KOKBr5k+QePHgwzGYzVCoVq0QREREAOJUcLykpQdu2bd0u27BcOVE40Wq1UKlUGDBgAO677z5kZWUFu0lEQcdzAia5oxbLlRMFnrT0k/REgigUqFQqqFQq2Gw2v0rH3n777QFoFVF4kya5a2pq/E5ySzuecCQ3hSPpuYaQzJZ22Kirq+N3m6KeP0nujh07Bqg1REQUrlq1aiXe5khuimRarVZM6LE6FBEJmOSOUixXThR40rlhEhISgtgSIte0Wi1qamp4ckAkk4Yjuf0lnUuPiUAKR57KlQP1F6X43aZoJ8zJHRsby3KbRC5wZBKRd6QjuYuLiz0uGxMTg+HDhyM+Ph5paWmBbhqRrKSD83gdi4gEzGhGKVflytVqNU8iiGR09uxZFBYWIiYmhiU5KSQJSW5/RnITUWPSJEVzktzSE3ZW2aFw5CrJLf0uM+5QtLNareIIOm9HcRMREbmi0+mQmJgIo9GIkpISOBwOj9d3+/Xr14KtI5KPtJMsk9xEjXFObooqrkZy8yIqkbxOnTqFkydPQqvVoqysDPHx8cFuEpETYVQdTw6I5NGwXLm/OJKbwl16ejqGDh0Km82G7OxsAI1HchNFs4qKCvEilC9J7vLychiNRthsNmRlZTn9rogiWbRetCXyVnp6OoxGI8xmM4xGo9/TJhGFMiF3UVJSgs8//xw//vgj/vjHP6J169ZBbhlR8HDQKpPcUevOO++EyWTCuXPnsHv3bgAsVU4kJ7vdLo7OiImJYblyCknChdG6urome3tLlZeXY/PmzYiNjUXnzp1x7bXXBrKZRGFDmuSuqqryezvSBCATGBSO9Ho9BgwY4PQYk9xEvxNKlQNASkqK1+vl5eXhwIEDAIC//vWvvKhLEY0XbYm8l5GRgZMnTwKoTwAyyU2RSDifMJvNOHnyJCwWCyoqKng8RBTlmNWMUomJidBoNDCZTBzJTRQAJpNJHImn1WqRmJgY5BYRNSbs9x0OB+rq6rxOplVXV+P8+fMAfLswSxTppOXKOZKbyBnLlRP9rqysTLzty7GUtGO6dFoAIiKKbunp6eLt4uJiXH755W6XraurQ3l5OSorK6HX63lOT2FDuGal0WjEc2aTyRTMJhGFlGitfMMkNzHJTRQA0iR3UlKSOB8lUShpOKrO2yQ3E3BErklHcjdnTu727dtj3LhxsFgsyMjIkKNpREHHkdxEv5MmuX0pVy49pxDO44miQbRetCXyljTJXVJS4nHZI0eOYMuWLQCAm2++GX369Alo24jkIpxPaLVasVM5k9wU7Vj5hknuqOdwOGC1WqFWq1munEhGlZWV4igl9oqlUOXvqDrpCFXpyFWiaCdXkjshIYHTXFBYs1gsqKurg0qlgkajgUqlYpKbSMLfcuXSJLfdbpexRUShhxdtibyn1+uhVCpht9ubTHJLS5kL0+wRhQNpklvo7MckNxEp/Vnp3//+N1JSUtC+fXunkzNBUVERrr76aqSkpODxxx9vbhspAA4dOoQff/wRhw8fFh9jkptIPiUlJWJv87S0tCC3JngYL0KbvwkHjuQmck2j0Yi/q+aUK49GjBeR5ccff8Sbb76JhQsX4tixYwBYrpxIShjJrVarfZrWSJrkjsZy5YwVRESuqdVq6PV6AIDBYPAYI6Ihyc14EZmkSW6hs19VVVUwm0QUUqK18o1fSe7x48eje/fuqKysxKJFi5yeKy8vx5gxY3D27FmMGzcOL7/8shztJJnt3bsXX3/9NfLy8sTHWK6cSD7SnrOtWrUKYkuCi/EitPmb5DabzeLt2NhYWdtEFO6E6gbNGckdjRgvIov0wqqQlONIbqJ6drtdvOCemprq02jVaC9XzlgRvaL1oi2RL4SS5TabDaWlpW6Xk3auitQkN+NFZJLOyS2cbzDJTdGOlW/8THKrVCr87//+LwAgJycHxcXFAOovet933304fPgwbr75Zixbtoz/ySFK6O0kPVFgkptIPtIkt9CbNhoxXoQ2f0fVcSQ3kXtCyfKamhq/S8kWFRXh9OnTOH/+fNQkMRgvIov0uy8k5TiSm6ie0WgUL8z6Oq2RtPpaNI7kZqyILvwMiXwjnZdb2D+6otFoxHMWo9EY8HYFA+NFZBKS3AqFQjwmitTvMBF5z68kNwCMHDkSAwcORHV1NV555RXYbDb89a9/RV5eHvr3749Vq1ax/HUIc5Xk5udFJB9pr9mMjIwgtiT4GC9CF0dyE8lPuGDkcDicfiu++O677/D+++9j9erVUTXHGONF5JAmuZXK+lNOjuQmqieUKgfqR3L7ItpHcgOMFURE7kivPTU1L7cwmttoNPrdMTfUMV5EHpVKJZ5bCJ9ddXV1xH6Hicg7fie5AeDFF18EAKxatQqTJ0/GZ599hiuvvBIffPCBWKpRcP78eTzxxBO45ZZb0LZtW6SkpGDXrl3NeXlqBiG5zZHcRIFRW1uLuLg4xMTERHW5cgHjRWhikptIftJ9mr8ly6XVEqS/02jAeBEZWK6cyD3p3KC+JrmFC7sAovqCLmNF9GG5cqKmSa89NZXkFublttvtEV3umfEisigUCvGcQjjHsNvtnCqM6P+L1uOlZiW5r7vuOtxxxx2wWCzYvHkzOnbsiI0bN7osuXXy5El89NFH0Ol0GDJkSHNelmTAkdxEgZWcnIxrr70WN998Mzp27Bjs5gQd40Vo8rd0LJPcRO4JI7kBeZLc0TYlAONFZHCV5Ga5cqJ60iQ3y5X7h7EiOrCEMJFvkpOTxXMHT+XKAed5uSO53DPjReQRktwpKSm4/vrrMWzYMKdKN0TRhsdLQLOymg6Hw+nC2wsvvIDWrVu7XHbQoEE4fvw4AGDbtm3YsmVLc16amsnVSG4muYnkUVdXJyYBExISGGzAeBGq/B1Vd+WVVyIzMxNmsznqEnBETZEmuWtqavzahvB7VKvVUXfCzngRGaTJN5YrJ3LWnHLlV1xxBbp06QKVShV18UGKsYKIqDGFQoH09HScO3cOlZWVMJvNbjulS5PclZWVyMrKaqlmtijGi8gjnFOkp6djxIgRQW4NEYWCZo3kfvbZZ/Hhhx+KydHly5e7fyFls16KZMZy5USBI50/VXriEM0YL0KTvwmHnj17YujQoRg5ciQ7cRA1IEe5cuH3GI2dSBgvIgPLlRO5JyS5lUqlWC7WW2q1GjExMVCr1VF9DMZYEX2itfwmka/S09PF255KlgvxJyYmJqKPyxgvIo9wTlFXV8fYQNRAtP4m/N57L1y4EEuWLEHr1q3xxRdfICkpCXl5efjss8/kbB8FiKty5UxyE8mjsrJSvM0kN+NFKGPpWCL5yVGuXLjQFG3zcTNeRA7pXMFCkls68pQxh6KVw+EQy5UnJSVF9WhsfzFWRI9o7shB5K+MjAzxtqckd7du3TBnzhzMnTsXV199dUs0rcUxXkQm4TqWw+HgOQUReLwE+JnkXr16NV544QUkJSVh/fr1uPbaazFjxgwA9WU/onluqHDBJDdR4BgMBvz000/49ddfUVBQEOzmBBXjRWjjqDoi+ck5J3c0JbkZLyKLNMktHRUjfKcZcyhaVVdXi/t4X0uVE2MFEVFTpCO5Pc3LrdFo3JYyjwSMF5FLWu2strYW1dXVET2vPBE1zeck9+bNmzFnzhzExMRg7dq1Ym+vhx56CHq9Hvn5+VizZo3sDSV5paSkICUlxSkwcE5uInmUlJSgqqoKZWVlfic4IgHjRejzZyS33W5HVVUVTwqJ3GhukttqtYq/r2hJcjNeRJ6hQ4di8uTJmDhxIhISEsTHhbjDJDdFq+bMxw3UV4z69ttvsXPnThw9elTOpoU8xoroFq3lN4l85W258kjGeBHZhPOJ2tpavP7663jjjTfw1VdfBblVRKEhWo+XfEpyf/PNN5g6dSoAYMWKFbjhhhvE5xITEzFnzhwAwD/+8Y+oTuyEgylTpmDq1Km4/vrrxcc4kptIHtITiVatWgWxJcHDeBEe/BnJbTKZ8Oabb+Jf//oXPvnkk0A1jShsSZPcNTU1Pq8vjPADomNObsaLyJScnIzMzExkZ2c7daSVzqFHFI2am+Surq7Gnj17kJeXh1OnTsnZtJDGWBGdWH6TyHexsbHifNslJSVRl/BgvIh8wvmERqMRr2NxJDdFMx4v+ZDkPnDgAO6//36xl8zo0aMbLfO3v/0N2dnZKCwsxFtvvSVrQykwrFareJsjuYnkcenSJfG2dD6kaMF4ET78SXJLE3CMG0SNxcTEiOWZ/blwUldXJ87RGukjuRkvoo+0XHm0XXQlAiDOxw3UV1fzlXQOb+m5fCRjrCAi8o1wHaq2thYVFRVul/vll1/w+eefY8OGDRFRqY3xIjoIHcGVSqU4YK+qqiqYTSKiIPP66nSvXr2anFs2NjYWv/76a7Mb5Ysff/wR//jHP/Ddd9/BarWiR48emDFjBu666y6vt1FbW4tFixZh3bp1OH/+PFJTU/HHP/4RzzzzjFOZl0jEJDeR/EpLS8Xb0ZjkZrwIHyqVCkqlEna73etRddKRqZE8hxeRvxQKBeLi4mAymfxKcqekpODxxx+H1Wp1mtc4EjFeRB/hQpTD4YDVamUlKYo6zR3JLT1nj4SEhDcYKwiI3vKbRP5IT0/H8ePHAdQPwnDXqerYsWPIz88HUF+xLTk5uaWaGBCMF9FBev4gJLyrqqrgcDg4opUoSrVoVlMoa3rgwAEAwJ49e1BaWoq4uDj84Q9/8Hl73377Le6++27ExsZizJgxSEhIwObNmzF58mScO3cODz/8cJPbsNvtGD9+PL7++mv07dsXt99+O06cOIHVq1fjm2++wVdffRXR5YalSW5eZCKShzBCQ61W+3XxihgvWopCoYBWq4XZbPZrJDeT3ESuCUnumpoavy/KsvOhdxgvQtPx48dhNpuhVCrRvXt38YJTwwoiPP+gaNPckdxCpRAgepLccmCsCE9MVhD5R5r0LC4uRufOnV0ul5iYKN6urKwM+yS3HBgvQp/0fEKr1cJisaCurg61tbW8RkVRL1o7Bbbo1bNJkyY53f/nP/8JAGjXrh1++eUXn7ZltVoxa9YsKJVKbNmyBVdffTUA4PHHH8eIESMwf/583HHHHWjfvr3H7axduxZff/017rnnHrzzzjviQfS7776LuXPnYsGCBVi0aJFPbQt1NpsNH330EWw2G44cOYKEhAQATHITycHhcIjloLRarfj7It8wXrQcjUYDs9nslLz2xGw2i7d5AkHkmk6nA1C///G2Awn5h/EiNO3ZswcXLlyAQqFA9+7dxcel5xucl5uikZDkTkhI8Ov8W1qunElu7zFWhL9ovWhL5A9pRcGSkhK3ywlzdwOc01jAeBH6pElu6bzcVVVVvEZFUYmdAn2Yk1sO5eXlLv98DRJAfU+oU6dO4Z577hGDBAAkJydj7ty5sFgseP/995vczurVqwEAzz33nNMXYvLkyejYsSPWr1/vVJo1EthsNpw4cQLHjx93KpfGJDdR89XU1Ij7DJ1Oh/j4+CC3KDwxXrQc4QTB22SDNBkulIYiImdxcXHi7XDcL4QTxovQJCTfVCqV0/9Bw5HcRNGktrZWnDPS32pP0ViuXA6MFeGJF22J/JOWliZ2ivKU5G44kpsYL8JBwyS3gB01iKJX2NZB3L17NwBg+PDhjZ4bMWIEgPoRBJ6YzWb88MMP6NKlS6NeUwqFAsOGDcP//d//4aeffsLAgQOb3Fa4qK2thdVqhc1mg91uF0+ObTZbWL2PaCBc/ONFwNDV8DMyGAyoqamB3W5HfHy816NjW1o09W5kvPBMoVCIMaGmpqbRxaSG3/HKykpxqguFQhFy7ycaMVaEHrVaLf5OysrKoNfrAXj3GZ09exZHjx5FTEwMrrjiiqDPscZ4UY/xomnSfZHZbIbVaoVSqWz0PoTfhtFodBpBRIHHeBFcFy9eFL//CQkJLn/jTX1GdXV14jZqampCZj/BWFEvGLFC2F448XZfJFy7EpYNt/cZzhgvQl9Tn1FSUhJKSkpw8eJFmEwml1MhxcTEiL8xg8HQIr8xxot6jBfecfU9dzgcTtOvSs+7MzMzW7aBxHgRAiwWi/g7MJvNjX7n4foZ+RIvwjbJfeLECQDA5Zdf3ui51q1bIyEhASdPnvS4jVOnTsFut+Oyyy5z+bzw+IkTJ5oMFBcuXAibntRms1nsoedwOMTe5IWFhRyVF6IuXrwY7CZQE4TP6PTp0+JvSqlUoqCgIJjNckmlUrnd70UixgvPjEajGBNOnz7tdh5g4Tt+7tw5cfnS0lKnXrQUXIwVocNkMom/kxMnTognHN58RgcPHsR3330HAB73Oy2B8eJ3jBfeu3jxIgwGAyorKxETE+N0LFReXi7+Ns6ePQu73R6sZkY1xovgOHnypPj9r62t9Xie4O4zcjgc4jYuXboUEucajBW/C0asAMI7XnhSVFQkft8vXrwYEt/3aMN4EfrcfUYKhUL8/fzyyy8u52yWnrOcPn064L8xxovfMV74Rvo9v3Tpkvi91Wq1TufdnDIyeBgvgqe4uFj8HRQVFbn9HYTTZ+RrvAjbJLfwwbnr/Z+YmNhkqRXh+eTkZJfPC9v2pmRLVlZWk8uEiqqqKiQlJcFms6GkpATx8fFQqVTo1KmT0xxfFHwWiwUXL15E69atmUgKUQ0/o0uXLuGKK66AxWJB37590a5du2A3MeoxXniWmZkpdszIyMhoVGK/4Xf80KFD4vvt1KmTy5NlalmMFaHn0qVLOHbsGAAgJSUFrVu39vozOnPmjPgba9++PeNIC2K8aB7pvkh4//Hx8U7f4cLCQhw/fhwAoNfr+f1uYYwXwXXhwgVxH9C1a1eX339vPqOUlBTY7XYkJSXxNxQEoRYrgPCOF572RWazWfy/yMjI4Pe9BTFehL6mPqMrrrgCxcXFAOpHbLv6/djtdiQnJ8PhcECr1fI3JjPGi+Zz9T3XarXi+05PTxdHpyYkJPA7HASMF8FXXFws/iYyMzMb/Q6i4TMK2yR3qAmncit1dXXiSD2HwwGVSgWtVsu5g0OYVqsNq+9YNBI+I5vNJgaTgQMH8nOjRkLtOxEfHy/GBJVK5bZ9wnfcbreLyycnJ4fc+4lmjBWhIyUlRfydWK1WpxNybz4jYd3ExER+plEsXD97rVYLhUIBtVqNmJgYp/eRkJAgfr8VCkXYvsdwx3gRHFVVVeL3PzMz0+Nn4Okz6tixIxwOB/R6PT9HAhDe8cJT22NiYsTfDPdbwcH/99Dn7jNq27at+PupqKhw+zmmpKTAaDTCbDbzs44C4foZS7/niYmJTtekRowYgYSEBCQmJkZsAi8cMF4Ej1ar9ep4KZI/o7BNcjfVU8loNCIlJcWrbVRUVLh8vqkeV+HK4XCIt4USgRqNJljNIYooJpNJvJ2YmBjElpCA8cIz6f7fm/lZbrrpJvTv3x+1tbWIi4sLZNOIwpZOpxNv19TU+LRubW2teJvTyLQsxgv5COcYDatESWNOXV1di7aJKNjKy8vF203tSzyZMGFC8xtDfmOsCA7pdSwialp6erp4WxjR7UpSUhKMRiNMJhOsVqvb6cvId4wXgdEwh9GhQ4cgtYQoNCgUimA3IeiUwW6Av4T5LIT5LaQuXrwIk8nUZN32jh07QqlUup3/Qnjc1dwZ4Uw69x2T3ETykia5ORdMaGC88Eza09WbJHdycjKys7Nx2WWXcYoLIjek1XGqq6t9Wlf6O2RP9JbFeCEfYa4/pdL5dNPXmEMUScrKygDUd4SSdoai8MJY0XJ40ZbIf9KKUCUlJW6X69ixI3r06IH+/fuH5VzNoYzxIjB4PkFEDYVtknvQoEEAgO3btzd67uuvv3Zaxh2dToc+ffrg2LFjOHv2rNNzDocDO3bsQHx8PHr37i1Tq0ODtAescJs99YjkUVxcjNraWjgcDk4BECIYLzzjqDoi+UmTF81JcnMkd8tivJCPcJG0YWcoXpSiaGW1WmE0GgEAqampQW4NNQdjBRGFA4VCIY7mNhqNbqtL3XjjjbjjjjswbNgwnnvIjPEiMKTnE7yGRURAGCe5hwwZgo4dO2LDhg04ePCg+HhFRQVef/11aLVa/OlPfxIfLyoqwtGjRxuV95g0aRIA4MUXX3RK/v7f//0fTp8+jbFjx0ZcL2uWKycKnAMHDuC///0v/vvf/7otSUQti/HCMyYciOQnV7lyjuRuWYwX8omLi0NcXFyjOb+kHWt5UYqiSUVFhbg/aE6pcgo+xorgYLlyIt9lZGSItz2VLKfAYLwIDIVCIZ4n19bW4vz58zhw4AD27NkDq9Ua5NYRBVe0Hi+F7fBdtVqNN998E3fffTdGjRqFMWPGICEhAZs3b0ZBQQHmz5/vNCfDCy+8gPfffx9Lly51msNq/Pjx2LRpEzZs2IAzZ85g0KBBOHnyJHJzc9GhQwc888wzwXh7AaXVatGnTx/U1tbi1KlTADiSm0gOdrtdHKERExPDcuUhgvHCM2lv7aaS3Ha7HQcPHkRMTAySk5ORlZUV6OYRhSWVSgWdToeamhq/R3JrNJpGpZ4psBgv5DNjxgyXj7NjFUUroVQ50Pwkd25uLi5dugSFQoG//OUvzWsY+YyxouWwXDlR80jn5b506RLnLm5hjBeBo9VqYbFYYLFY8P333+O3334DAFx55ZXsTEhRh8dLYTySG6gvqbJt2zb069cPmzZtwrvvvouMjAy8++67ePjhh73ahlKpxNq1a/Hkk0/i0qVLWLZsGb777jv8+c9/xpdffolWrVoF+F20vISEBNx8880YPnw49Ho9ACa5ieRgMpnEEXiJiYn8XYUQxgv3fClXXltbi61bt+Ljjz/Grl27At00orAm9Kb3NcktxBGWCwwOxovAYpKbopU0yd3ccuUGgwFFRUUoKipqbrPIT4wVRBQOpEnupkZy2+12p4pSJA/Gi8AQrmPV1dU5DTAymUzBahIRBVHYZ2D69OmDDRs2NLncW2+9hbfeesvlczExMXjyySfx5JNPyt28kCZNZrBcOVHzGY1G8YJtcnJykFtDDTFeuOZLwkF60tuwBC0ROROS3LW1tT6VTevYsSNMJhN/Y0HEeBE4vnSsIook5eXl4u3mjjAS5rp3OByw2+2s+hEkjBUtK1rLbxI1hzTJXVJS4nKZmpoavPvuuzCZTLjsssswduzYlmpe1GC8kJ/QIdxisSA+Pl58vKqqKlhNIgoJ0Xq8FPZJbvKf9IIrk9xEzVdSUiIGk+aO0CBqKb4kuc1ms3ibo0yJPJOebPsyL/eoUaMC0RyikMCR3BSt5BzJLSS5gfpzeunviiiSsPwmUfMI04xVVFSI16sa/q5iY2NRVVXlNP0eUagT8hg2m82pczhHcv8/9u48Psry7Pv/d2ayh4QsJIRNQFkUQRSrCIiIWotFUFHci7elIAgK4q19XNq64G37KIIVFRH6KN6oiIVKQMEFUIQgrVYRxYAsyhIgC9nXWX5/8JvpTDJJZpLZ5/N+vfJiMnMtZ5iZ67iu6zjP40Q04nyJJHdUcx49QVlloP2OHz/ueGyfCgAIdd6MqnNOcttHqQJwz/k74k2SGwh3NTU12rBhg4xGo7p27arzzz/f8VpsbKwMBoNsNhsjuRFV7Enu2NhYl7KabeGc5LZYLO3aFgAgsmVnZ6usrEz19fUqKytrUk3EYDCoQ4cOKisrU3l5eXAaCXjJuYOf8wAMktxAdCKzGYUOHz6s5cuXq7y8XEeOHFFqaiojuQEfKCoqcjwmyY1wwUhuwD8aj+SmnCyiRUNDg77//ntJpxJwzklug8Gg2NhY1dfXM5IbUcNqtaqsrEzSqVLl7R1t4dxB3ZvpMIBwFq3lN4H2ysrK0t69eyWdmpfb3ZQZqampKisrU01NjRoaGrhHjJDnfD/K+fNKkhvRLlrPl7jbFoWsVqusVqsaGhocH3xGcgPt55zkdp77CAhlbU1yM18w0DLnkdzV1dVBbAkQWM4jS9117rDHHZLciBbl5eWO74UvpjRiJDeiBeU3gfbzZF7ulJQUx2NGcyMcOCe2nRPezMmNaMT5EiO5o5LVanX8a/8S0EsPaD/nufays7OD2BLAc96UK6+rq3M8ZiQ30LLG5co9KU97/PhxrVy5UvHx8Ro0aJAuuugifzYR8AvnpJtzMs7OHncoV45oUVpa6nhMkhsAEEjO96aaS3KnpqY6HldUVFCZECHPebCG0WiU0WiU1WolyQ1EKUZyRyH76G17sltiJDfgC/Ykt9FoVKdOnYLcGsAzjOQG/MO5XLmnI7lra2tVUVGhoqIil+8bEE6crzHcJbkZyY1o49wRliQ30DbRWn4TaK+MjAzHPd8TJ064XcY5yW2fXgMIZc6DNcxms+Pam3LlQHQisxmFnJPcjOQGfOe0005Tenq6EhISlJGREezmAB6JiYmRwWCQzWZrdVQdSW7Ac20pV+6c9OPcDOGqtXLl9s+2xWKRxWJxmwgHIonzSG53c6F6y7mDOkluRDLKbwLtZzQalZmZqePHj+vkyZMym81NBjo5lyuvqKgIdBMBrzlXFqyvr1d6erpiY2OVkpIim81G/EDUitZOgSS5oxDlygHfa2hoUH19veLj49W9e3eqIyBsGAwGxcXFqa6urtVRdbGxserQoYNqampIcgOtaFyu3BNMCYBI4OlIbunUTSnn7woQiXw9krtPnz5KTk6WyWTyaCoMAEB0y8rK0vHjx2W1WlVUVKScnByX151HcjMnN8KB8/VEXV2dbrvttiC2BgguOnWQ5I5K9htPzr2+ScgB7eNcEse5FywQDmJjYz1Kcl922WW67LLLorZnIOAN53Llnia5nb+DzhfuQDhpbU5uktyINs5TGjknEtrq9NNP1+mnn97u7QDhhOsPoO2c5+U+ceJEkyQ3I7kRbpwH67VWkRBA5COzGYUoVw74nvOFAEluhBt7wsHTiwN6CQKti42NVWxsrBoaGjwuV85IbkQC55HcLZUrl7gphchns9kc5co7duzo9jsBwD2uOQDfyMrKcjwuKipq8npSUpLGjx+vlJQUn0yrAfhb406zAE6J1k6BJLmjkLty5YzkBtrnxIkTOnjwoKPsMxBO7BcIXBwAvpWUlKSysrI2jeQmyY1w5e1IbiCSVVVVOT7nvihVDgCAt5yT3CdOnGjyusFg0Nlnnx3IJgHt0nhObgDRjcxmFHJOctsxkhton6NHj+rw4cOS3F80AKHMHgMsFossFovbpAQA7zknuT3pUUu5ckSCDh06aNCgQbJYLC43Ve1IciOa+Ho+bunU+VpdXZ0sFovi4+OJFwCAFnXo0EFJSUmqrq5WYWFhsJsDtJtzHqO+vl7Hjx/Xli1bVFVVpYEDB+r8888PYusABBpJ7ijUo0cPTZw4UZs3b9Y333wjiZHcQHsVFxc7Hnfq1CmILQG85+n8qH//+98VGxurTp06afjw4YFqHhC27N8lm83mUZUP52VIWiBcde7cWT179mz2dcqVI5rYS5VL8lkJ2K+//loffvihJOnqq6/WoEGDfLJdAEBkMhgM6tSpk37++WdVVlaqurpaSUlJwW4W0GaN72FZLBbt3btXkprMOQ8g8jEhVBRKTU1Vnz59lJ2drYSEBEmM5AbayznJ3blz5yC2BPCeJ6PqzGaz9uzZo++++0779+8PVNOAsOZ886i2trbV5RnJjWjASG5EE3+M5HauuOM8PQAAAM3Jzs52PHZXfbCiokL79u3Tv//9b5WUlASyaYDXnK8nGhoalJyc7Pi9qqoqGE0CgsY+HXE0Y/huFDObzY7HJLmB9nG+geV88QCEA09G1TmPMLV3kALQMucktyfzcg8ZMkS9e/dWXV1dsxUVgHDHSG5EE5LcgG94Mu0LgOY5TyFTWFioXr16ubz+448/av369ZKkq666ShkZGYFsHuCVxp1mO3To4Pi9srIyGE0CEEQkuaOY800lypUD7VNeXi7p1HfJVzewgEDxZFSd8yjU+Ph4v7cJiATOiWpPRnL36tWryQ0nINIwkhvRpKyszPG4Y8eOPtmm87U7SW5EMkYmAb7TOMndWEpKiuOx/f4WEKqcryfq6upkMpkc886T5AaiD5nNKFRWVqaTJ0/q+PHjjkQ3SW6g7Ww2m+MiID4+3qVMDhAOPEk4OI/kZoQp4Blvy5UDkeDrr7/W9u3bZTQaNWbMGPXr18/ldZLciCb2kdwpKSk+q57mPJLbuTobAADNycrKksFgkM1mc5vkTk1NdTwmyY1QZzKZZDKZZLFYHLmN5ORkVVdXq6qqSjabjY5SiErRWvmGObmj0N69e/XWW29p27ZtqqiokES5cqA96urqHGVoU1NTZTRyaEV48bZcOSO5Ac94W64ciAQNDQ2qqalRVVWVrFZrk9cpV45oUVtbq+rqakm+K1UuUa4c0Slab9oCvhIXF6e0tDRJp0ZyN/5OOY/ktt8rBkKZ/b6UvdOsvWS52WymgzmiCh06SHJHJfvNJuebTozkBtquvLzccZPWftEAhJPGpZ7ccb5IYE5uwDPelis/fvy4SkpKHEkRIBw5J92ck3F2jORGtCgtLXU89uU1AkluRAtu2gK+ZS9Z3tDQ4BKjpFPX+PZzNEZyIxzYO87aryecq2pWVVUFpU0AgoMkdxSy99azWCwyGAyKjY3l4gFoh5KSEsdjktwIR56MqmNObsB7zhfarSW5bTab/t//+3965ZVXtHLlSn83DfAb54607qrbkORGtLCXKpcYyQ20FyO5gfZznpf7xIkTLq8ZDAbHaO6Kigq+cwh59muKxiO5JTEvN6JWtB67SXJHIeeR3AaDgVHcQDtVV1crPT1dycnJysnJCXZzAK95Oyc3I7kBz3gzkruhocFxQeL8nQTCjXOS291IbsqVI1r4K8ntfP3OnNyIZAzGAHwrOzvb8bhxklv6z7zc9qlngFBmv2ZuaGiQ1WolyY2oxfmSRHYzCjUuV8583ED72Gw2nXXWWYqJidGoUaOC3RzAayS5Af9ISEhwjGRtLcnt/B0jyY1w5jyylJHciGaUKwcAhBLnkdxFRUVNXm88L3dSUlJA2gW0hfM1RUNDg7p27aqLLrqIAUhAFCLJHYXso4TsI7ndjbAA4DnnuVOdew4C4cKTUXVdunTReeedp9raWkcPbwAtMxgMSkpKUn19faujIZyTfSS5Ec68mZObkdyIZP4ayZ2enq477rhDJpOJBASiRrSW3wR8KT09XTExMTKbzS2O5JZOzcvduXPnQDYP8IrzNUVdXZ26deumbt26BbFFQPBF6/kSSe4oZB/B7TwnN4C2I8mNcOfJqLp+/frpnHPOCVSTgIiRmJio0tJS1dbWtnjB4TySm3nvEc5aS3IbjUbHDVaS3Ihk9iR3UlKST6vgxMbGqmvXrj7bHhCqKL8J+JbRaFRWVpYKCgp08uRJNTQ0uNwTTklJUXx8vFJTU/n+IeQxBRJwCsdrktxRyWazOX4kypUD7VVVVSXp1AVDcnJykFsDeI/SsYD/2EfZWSwWNTQ0uMzT7cz5u0eSG+HMuTNHcxWj4uLiZDabiTmIWA0NDaqoqJDk21HcAAC0R6dOnVRQUCCbzaaioiJ16dLF8drgwYN17rnnBq9xgBecr5m5pgCiG0nuKGS1Wh2juQ0Gg2Ji+BgA7fHVV1/JbDZzAwthix6wgP84l5Ktrq5uttw/5coRKVqbk1v6T9wh5iBSlZWVOR537NgxiC0BIkO0lt8EfC07O9vx+MSJEy5JbkYDIpy4G6xhtVpVXV2t2tpaderUKVhNAxBgZDejkM1mc0lyM5IbaDur1ary8nJJpxIZzd3MBUKZJyO57XEDgHecR263NC835coRKc4991z169dPFoul2Wlc7HGHUReIVP6aj1s6dU62e/duWSwWJSUlqU+fPj7dPhAqSLgBvuec5C4sLAxiS4D2cc5n2K8plixZouLiYsXHx2vOnDnBahoQNNHaKZAkdxQaPXq0Bg8eLKvVqsrKSkZyA+1QWVmp+vp6xcXFMUoDYcs5yd3cqLqXXnpJBoNBWVlZmjRpUqCaBoQ955HcLSW5nZN9dEBEOOvSpUur8w87J7ltNhuJDEQcfya5JWnNmjWSpO7du5PkBgB4LCsry/GYJDfCmbvBGklJSSouLlZdXV2TOeeBSMW1NEnuqGQwGGSz2WQymRjJDbRTUVGRo5dUWlpacBsDtJHBYFBcXJzq6+vdjqqz2Wyqra2VyWRiRDfgpcblypvDSG5Ek8bTZFCiH5GmtLTU8djX1whGo9FxTe88PQAQyaJ1ZBLga8nJyUpOTlZVVZXbJPe2bdt05MgRVVVV6Y477iB5gpDlbrCGcxWpyspKppUEogRJ7ijlPFLPZDIFsSVAeDt27JjjcUZGRhBbArRPS0nuhoYGx40lkm+AdzwdyX3hhRdq0KBBqq+vb7bEMxApGo+8IMmNSOPvkdwxMTFqaGggyY2IRnIN8I+srCxVVVWpqqpKlZWVLtcehw8f1r59+ySd6qCbnJwcrGYCLXK+frB3GCfJjWgXrZ0CmTw2SpnNZsdjRnIDbVdUVOR4nJmZGcSWAO1jjwXuypU7J75bK0ELwJXznNwtjeSOiYlRSkqKMjMz6UyCsFZYWKiCggIdP3682YvsxiO5gUhjT3LHxcX5JUFg76jufF0PAIAnnOfldr6nJUkpKSmOx+Xl5QFrE+Atd+XKnc+5qqqqAt4mIBjoFEiSOyrt3r1bn3/+uX766Sc1NDQwJzfQDiUlJY7HznMbAeHGeX7UxiijDLSdpyO5gUjx0Ucf6bXXXtPf/va3Zqe4cHdTCogUVqtVZWVlkk6VKvfHjSd7kpuR3IgW0ToyCfCHTp06OR6fOHHC5bXU1FTH44qKioC1CfCWJ+XKAUQHktxRaN++ffryyy915MgRWSwWRnID7VBcXOx4TJIb4cweC8xmc5OkhHMCwnlUKoDWkeRGtHFORBiN7i83SXIjkpWXlzvOpfxVJpMkN6IBI5MA/3Aeyd04yc1IboQLd9cTzkluRnIjGkVrp0CG8EYhq9XqksAgyQ20nfN8ezk5OUFsCdA+jS8QnMuSM5IbaDtPy5Xv3LlTNTU1io+P1znnnNNschAIdfakm8lkajZBQblyRDJ/z8ctkeQGALRdp06dZDAYZLPZmpQrZyQ3woW7Obmdy5Xz+UW0oFNgGI/kLi8v18MPP6yBAwcqOztbgwYN0h/+8AevS1GkpaU1+zN9+nQ/tT64bDab42LYYDBQrhxoB3spQqPRyJzcIYp44ZmWRtU5J7mZkxvwjslkcnxvWhrJ/dVXX2njxo1av349FylBQrzwDeckd3MYyY1IVlpa6niclpbml33Yr+GZkzs4iBeBF60jkwB/iI2NdXTCKiwsdBkI5ZzkZiR3+xAr/Mtdp1lGcgPRKSyzm1VVVRo7dqy+/fZbXXbZZbrhhhu0c+dOvfDCC9q6davef/99r27C9+jRQ7feemuT5wcNGuTLZocM55HcJLmB9snOzlZDQ4MyMjIo4xyCiBeea2lUnXMCgpHcgPfs8aGlJLf9exYfH0+SOwiIF75DkhvRjpHckY14ETicDwH+k52drZKSEpnNZp08edIxaINy5b5BrPA/53tT9uuJxMREmUwmWSwWktxAFAnL7Obzzz+vb7/9VrNnz9Zjjz3meP6xxx7TggUL9NJLL2nOnDkeb++0007TQw895IeWhiabzebSS48kN9A29pLOWVlZGjBgABfhIYh44bmWEg7MyQ20j31e7traWlksFrfJP3vFBKaRCQ7ihe/YrzNaKrlPuXJEMuckt79GcicmJio5OVkmk0k2m43rkAAiXgCIBFlZWfrhhx8knRrNbU9yx8bGKikpSdXV1SS524FY4X8xMTGOsvv2e1YGg0G333674zwJiDbRWvkm7MqV22w2vfHGG+rQoYMeeOABl9ceeOABdejQQcuWLQtS68JD45Hc3EwF2sa5xJBzSRyEBuKFd1pKcvft21fXX3+9xo8fr+zs7EA3DQh7nszL7TySG4FFvPAt+8jSlpLcjORGJLOXKzeZTC5lX33p5ptv1r333qsZM2aQ4A4g4kXwROtNW8BfsrKyHI9PnDjh8pp9NHdlZaXLICl4hlgRGAaDwXFN4Xw90bVrV6Wnp7tcbwCRjGuBMBzJvW/fPhUUFOjyyy9v0iMnOTlZQ4cO1SeffKLDhw+re/fuHm2zrKxMr732moqLi5Wenq6hQ4fq7LPP9qpdtbW1Xi0fTHV1dWpoaJDVanX0eAqn9kcTe5Dm5l9oKioqctzITUxMDKvvUTTMq0y88I7NZnPM61hZWeloZ319vVJTU9W5c2fHRUKo/g3RilgR+uwdCi0Wi06ePNmkg6HVanUkv41GY0h9x4gXxAtP2Y9BDQ0NMpvNLV5jOMecqqqqsPtbwxXxwv9sNpsKCwtlNpuVkpLi9f91uL5H0RArJOKFr3j6Oa+vr3fEivr6+rD7O8NZuB6Lokl736PU1FTH9+vo0aMu36++ffuqe/fu6tChg6qrq31aATQa4kWoxgop8uKFwWCQ2WxWdXV12P1tkYJ4EXx1dXUtni+F63vkTbwIyyS3JJ1++uluXz/99NP1ySefaN++fR4Hil27dmn27Nkuz11xxRV6+eWXXXq2teTo0aNhMx9WUVGRKioqHKUBS0tLdejQoSC3Ci05fvx4sJsAN7777jsdP35csbGxqqmpCZvvkclkavYYGkmIF945efKkoxzZzz//3KTXK8eh0Md7FLrsFxlVVVXat2+fozS5XV1dneP7l5qaGjLxhHghx/PEC8+VlZWpvr5eRqOx2c9yYWGh4zN/5MiRkPnMRwvihf9UVVWpuLhYktSxY8c2f7bD6T2KllghES98rbXPeVFRkSNWFBUVESuCIJyORdGqre+RzWZTdXW1zGaz9u7d6/L9ysnJcTwuKChodxvtoiVehGqskCIvXtjL6tfW1hIjgox4ETzHjh1znC8dP3682e9COL1H3saLsEty29+wjh07un3dXg7M03lDZs6cqfHjx6tPnz6KjY3V7t279cwzz+ijjz7STTfdpI8++sjtvImNde3a1cO/IPjS09OVkJCg2NhYGQwGdenSRV26dAl2s+BGfX29jh8/7jKCEqEjLy9P+/fvV0NDg84//3z16NEj2E2CE+KFd06ePOn4P8nMzHR8njkOhT7eo9B3+PBhffPNN0pOTlZaWlqTeFFRUeH4/uXk5BBPAox44Rv2Y1FSUpISEhJcYkljCQkJjv9Xd98J+Afxwv8OHz7s+GyffvrpXn+2eY9CG/HCNzz9nMfHxzv+Tzt16kSsCCCORaHPF+/RGWec4Uhi8177TqjGCiny4kV2drajUm337t1lMBhUXFysw4cPq7KyUv369fOqEwC8R7wIvpqaGsdxJTs7u8n5UjS8R0FLcj/yyCNeDZGfNm2azjjjDJ+3Y+7cuS6/X3jhhVqxYoXGjRunrVu3at26dRo/fnyr2wmncivdu3dXdna2amtrZTAYHDeiELri4uJ4j0JQWVmZY77JnJwc3iM/IV4ERocOHRxlyAwGg0s7Dx8+LKvVqo4dO7r06kZoIVaELvsFh8lkksViafI+VVZWOr5/HTp04H1sI+JFaJg5c6ajJH9zf0NKSopL6ctw/VvDFfHCf5zLunbu3LnN/8+tvUdff/21fvrpJ1ksFv3yl790zJ8KzxAvQkNrn/OEhATH9yk2NjZs/85wRrwIfe15j7p27arCwkJJpzrdduvWzZdNC3uRFiukyIsXSUlJjjgRExOj2NhYFRYWauPGjZLUYqdb+BbxInji4+Md34OW3odIfo+CluR+7bXXVFVV5fHy48eP1xlnnOG4SVhWVuZ2OedSj21lNBp1xx13aOvWrfriiy88DhTh4rLLLlNJSYlSU1NVXl7u07lVgGhiL0UonbqJBf8gXgSGc2++xhdyn376qUwmk9LS0nTPPfcEumlA2EtMTHQ8ts+97cy5fHmk9qwNBOJFaIiPj2/14rmlmAOEs5MnTzoep6en+20/R48e1ffffy9JGjlyJEluLxEvAODUiD+7wsJClyS31WpVRUWFbDab0tLSgtC64CNWhL74+HjH4/r6esXGxrrMg15ZWRmMZgFBY7PZgt2EoAhadvPIkSNtWs/eI2r//v1uX7c/396eU5mZmZLc34iMBPbJ6CWR5AbaqLS01PHY+eIAvkW8CAz7qDtJamhocHmtvr5eiYmJEdvjD/C3pKQkx+OampomrxuNRnXt2lV1dXUkKtqBeBE+SHIjUjlfH/gzye1cljQc59YMNuJF+InWm7aAP3Xq1Mnx2D6iWzqVnF20aJGsVqvOPPNMXXfddcFoXtARK0Kfu/tYHTp0cDznTScFIFwZDIZgNyHowi67ecYZZ6hLly764osvVFVV5dI7p6qqSl988YV69uyp7t27t2s///rXvyRJp512Wru2E6qcExjOAQGA5+y9MmNiYqK2Z2soI154p7mEg9VqVUNDgxITE116yQLwXGsjubt06aI77rgjkE2CE+JF4JlMJhmNRkeMASKFPcltMBianYvTF5yT3M4d2OFfxIvA4qYt4F/OgzVOnDjheNyhQwdHx5KKioqAtyvcESsCx/k+lr06GiO5gehjDHYDvGUwGPSb3/xGlZWVeuaZZ1xee+aZZ1RZWdnkJmF1dbX27NmjQ4cOuTz/3Xffub2p8sUXX+j5559XbGysrr32Wp//DaHAfiFsNBodcwoD8JzNZnOUGEpKSuJ7FIKIF95pLsldW1vreMxIbqBtWktyI7iIF75jNpu1detWbd26VT/88EOzyxkMBkfcYSQ3Iom9XHnjeed9zXnbjOQOHOIFgEiSlJTkGPVaVFTkSGybTCZHorC5kttoHrEicNzdx0pOTnZ0kmIkN6JNtFa+CbuR3JI0a9Ysvf/++1qwYIF27typwYMH65tvvtHGjRs1ZMgQTZ8+3WX5L7/8UuPGjdOIESO0bt06x/MLFy7Uhx9+qIsuukjdunVTbGysfvjhB23cuFEGg0HPPvusevfuHeg/z+9Wr16tzz77TJWVlerXr1+wmwOEpcrKSkfyj9KyoYt44bnmypU7zxXMSG6gbeLi4hwJCXflyhF8xAvfqK+v17Zt2xQTE6N+/frpzDPPbHbZ2NhY1dbWkuRGxKipqXEc4/1ZqlySSwdbktyBRbwIjmi9aQv4W1ZWliorK1VdXa3KykrH/a3U1FRVVlaqqqpKFovFpYIIWkesCAznJLf9PpbJZFJSUpKqqqqoRICoQOWbME1yJycna926dfrzn/+s3NxcbdmyRZ07d9bMmTP1+9//3mW0TEt+/etfq6ysTLt27dLmzZtVX1+vzp076/rrr9f06dN1/vnn+/kvCY7i4mKVlZWpvr6e+biBNnIu5ZSamhrElqAlxAvPNTeS2znJzUhuoO3s3x9Gcocm4oVvWK1Wx+PWqtzY4w7lyhEpnOfj9vdURozkDh7iReBw0xbwv+zsbB04cEDSqXm57Ulu+782m00VFRVM0eclYkVguCtXLp36/6+qqlJVVZVsNhvxBIhwYZvh7Nixo55++mk9/fTTrS47cuRIlwtOu3HjxmncuHF+aF1os1qtjhtQ9MQD2sY5ye3P+fbQfsQLzzR3cUC5csA3EhISVF9fr5qamiYX2v/85z/1ww8/KD4+Xpdddpk6deoUxJZGL+JF+zkn2zxNctfX13PzCRHBXqpc8v9IbubkDi7iBYBIkZWV5XhcWFio008/XZLrYA6S3G1DrPC/5gZrdOjQQSdOnJDFYlFtba3HnQoAhCcmkY1CNptNVqtVBoOBkdxAGxUVFTkec7KPSGA0Gh0xwXlUHUluwDfs3x+r1dqkZHlxcbEOHz6sffv2kaxAWHMuJ9taZ1r7NBlWq5WRqIgIwUpy8/1BNKBcOeAfzklu58EcztPylZeXB7RNgKeam3bPPqe8dGq6SSBaROv5EhnOKGS/kWQymUhyA22Unp6uESNGqLq6WgMGDAh2cwCfiIuLk9lsbrZcOXNyA22XkJDguEFUU1OjpKQkx2vO3zm+Zwhnzsm21pLcjUdecF2CcEe5csC3qPAB+F+nTp1kNBpltVpVWFjoeN45yc28xghVztfOztfU6enp6tSpk0uyG4hUnC+R5I5KziOEuJkEtE1lZaUMBoPi4+OVkZER7OYAPmHvBevcA9ZisThKzjKSG2g75xJp1dXVyszMdPzufEHunPgDwo3znNzeJrmdO34A4SiQI7nT09N19tlnKyYmxu/7AgBErpiYGGVkZKioqEhFRUWyWq0yGo0u5coZyY1Q5TyS2/maesSIERoxYkQwmgQgCMhwRiF7kpty5UDbOZe74aYsIoXz/Kh2Q4YMUadOndSlSxc+60A7OHcSqa6udnnNuWICSW6EM29GcjdXXhAIV/Ykd3Jyst+rcvTs2VM9e/b06z6AUBKt5TeBQMjKylJRUZEsFotKSkrUqVMnRnIjLDQ3JzcQraL1fIkMZxQiyQ14b/369Vq1apW2bNmikydPymw2y2azKTExUbt27dJtt92m8ePHB7uZQLvYLxAaGhocPbilU/EiNja21YQFgOY5J7kbz8ltvyA3Go2cmyGsOY/ktseQ5nBTCpGkoaHB0QnW36XKgWhB+U0gMLKysrR7925Jp+bltie5b7/9dqWkpLgkvIFQ0ly5ciCacL5EkjsqkeQGPFNSUqL7779f69atU0NDg9veUJWVldqwYYM+/PBDxcbGauzYsZo3bx4lzBGWGo+qY25gwHcalyt3Zh/JHR8fzwUKwpo3SW5GciOSBHI+biAaRevIJCAQsrOzHY/t83IbjUb16NEjWE0CPML1BABJavnOAyKS85zcjMoD3Fu0aJH69eun1atXq76+vtWLapvNpvr6eq1evVr9+vXTokWLAtRSwHcYVQf4T0vlyu0X5JQqR7iLjY1V165dlZOT4zKXozvEHESSQM7HDUQLOv4BgZGVleV4bE9yA+HA+XrCeQowi8Wid955R//v//0/rV69OhhNA4IiWjsFMow3Cv3iF7/QsWPHJImR3EAjZrNZw4YN048//tjmwGA2m/XQQw9p6dKlysvL43uGsOGuF+znn3+uw4cPq3v37rr88su52QS0kSdzclM9AeGuc+fO+sUvfuHyeW8OSW5EkkCP5D569KhWrlwpi8WiIUOG6NJLL/X7PgEAkaljx46Ki4tTfX29Tpw4EezmAB5r7nrCZDLp8OHDqqur4zoDEY/7tIzkjkpnnXWWunfvrpycHJJvgBOz2ayzzz5be/fubXfPJ5vNpr1792rgwIEu1ROAUObuAuGHH37Qd999p6+++ooTJ6AdmpuT22q1OjqVOHc0ASId5QURSYIxkru6upqbt4ga0ToyCQgEg8HgGM1dVlbm6IBbVFSkr776Sp9++qmKioqC2UTALaPR6LimaHw9kZycLEmqqqoKeLsABBZJ7ijknHAjyQ38x7Bhw3T8+HGfbvPYsWMaPny4T7cJ+IvzKFL7DVP7Ba4no/IANC8+Pt4xR7HzSG6bzaZRo0Zp2LBhOvvss4PVPCDgGMmNSBLoJLfztGMWi8Xv+wOCgQ62QOC4K1l+4MABbdiwQdu2bVNBQUGwmga0yH5N4VyuXPpPkpsOgUDkI8kdhZx7NpHkBk556aWX9OOPP7a6XHZ2tjZv3qwTJ07IYrHIZrPp1VdfbXGdvXv3Mkc3wkLjUXU2m40yyoCPGAwGJSYmSnJNcptMJg0fPlyXXnqpzj///GA1Dwg4ktyIJPZy5XFxcUpKSvL7/pyv46kaBQBor+zsbMdje5I7NTXV8VxFRUXA2wR4ormR3CkpKY7HjOZGtIjWyjckuaOMzWZTWVmZ6uvrZTabSXIDkkpKSvTHP/7Ro0DQt29fjRo1Sunp6SorK/No+zabTY8++qhKSkra21TArxonHMxms2N0EEluoP2ck9zRevGByHbgwAEtX75c//u//6v9+/e3uCzlyhEpLBaL47ogPT09IKNPnUdyW61Wv+8PABDZ3I3kdk4SlpeXB7xNgCfs97Ead5q1j+SWpMrKyoC2CQgkKt+Q5I46NptNb775pnbs2KHdu3e7XBwD0er+++/3eATEN998o0GDBik2Nlb33nuvx/swm8164IEH2tpEICAaJ7lra2sdv1OuHGg/e5LbbDaT1ENEqq6u1tGjR3Xo0CGXigXuMJIbkaK8vNyRaA7UfNyUKwcA+JJzkvvEiROSGMmN8GC/pnAepCFJHTp0cDwmyQ1ENpLcUcZmszkuwA0GAyO5AUnr1q3zeNnKykrt2rWrTfvJzc1t03pAoDQeVeec5GYkN9B+ziVs7QnAhoYGVVdXk6RARHD+HLfWmZYkNyJFoOfjlly/X5QrBwC0V2JiomPkdmFhoWw2m5KTkx3xhpHcCFXNXVM4j+SmXDmiRbRWDCTJHWWsVitJbsDJ+vXrAzaarqGhQevXrw/IvoC2YCQ34F/2kdzSf5LcP/74o55//nn93//7f7Vjx45gNQ3wCeeyya0luSlXjkhhn49bktLS0gKyT0ZyI9pE601bIJDs83LX1taqoqJCBoPBMRqWkdwIVc0luRnJjWhBuXKS3FHHOcktiSQ3ot6qVasCdsFss9m0evXqgOwLaIvGCYe6ujrH74zkBtrPOcldU1MjSS7fM+cLdCAcOSfbjMaWLzUZyY1I4TySO1BJbufreEZyI1Jx0xYILHfzcttLltfU1HC+hpDkfE3h3HGWJDcQPchwRhmbzea4+cRIbkDasmVLRO8P8AYjuQH/aq5cuR1JboQ7RnIjGgWjXLlzJxLn7x0AAG3VeF7uM844w1HCXDo1mjszMzMYTQOa1VzH2dTUVA0bNkwdOnRwVCkAEJnIcEYZypUDpzp71NXVqaqqSsXFxQHdt/NNMCDUNL446NChg/r376+CgoKAjUwCIpm7cuVUTEAkcU62tTSSOz8/X3l5eVqzZo2Ki4tlMpm0ZMkS9e7dW6NHj9awYcPUv3//QDQZaDd7uXKTyeSSDPAng8GgcePGyWg0unSgAiIV5coB/3NOBNpHcnfs2NHxXHl5OUluhJzmktzx8fG69NJLg9AiAIFGhjPKWK1Wl5HcrY2wAMKFc+K6srJS1dXVqqqqavbH/j0I9Bx2XJwjlDUeVderVy/l5OTo0KFD6tGjRxBbBkQG50SEvVy584W483cQCEctjeSuqanR8uXLtXjxYpWUlKikpMRl+YKCAu3cuVO5ubnKzMxURkaGpkyZottuu82lgwgQSmw2myPJnZaW1mqZfl8aOHBgwPYFBAPlyoHAyszMlNFolNVqdSS509PTlZWVpdTUVAZKISQ5X0NTUh+ITkSnKMOc3AgnNptNtbW1qq6u9ipxHcq4UEcoY35UwL+ck9xVVVWSGMmNyNJcknvr1q2aMWOGCgoKXD7zzW2jsLBQhYWFeuSRR7Rw4UK9+OKLGjFihN/aDbRVRUWFo9x+oEqVAwDgDyaTSZmZmSosLFRxcbEsFosSEhIUExOjdevWaeHChbJarTIajVTfQchwvobmPhYQnchwRhmbzUa5cgRVMBPXBoNBSUlJSk5OdvzbsWNHlZSU+GwfreHmF0IZSW7Av5xHo7obyU2SG+HO+bzNPhLovvvu09q1a9s0RUxdXZ0OHjyoSZMm6eqrr9b8+fMDOlIWaI19FLckpnYB/IiKaEBgZGVlqaCgQF999ZUuuOAClZeXN6m+I4nqOwgZLY3ktlgsjvvLnTp1onIaIl60ni+R4YwyJLnhD40T11VVVS0mr/2duO7QoYPjsfNPUlJSkxuja9eu1TvvvOPVPlesWKGMjAzl5ORIkq666ip99NFHkqQ777xThw8fbnbdkSNHevkXAoFjMplkMplksVgco5IA+E5rc3I7dzQBwtFpp52mnj17KiYmRomJiZo0aZI2btzo+Ly3VXFxsVauXKni4mItW7aMRDdCxsmTJx2PA92ZtaioSHV1dbLZbOrevXtA9w0EAlXQgMA7duyYlixZosrKylbv3VF9B6GgpcEaH330kf79739Lku644w517do1oG0DAoHzJZLcUSclJUXDhg3TsWPHmJMbLWopce1uBHYoJa69MWHCBK1cudKrnk4TJkxw6SDSrVs3devWTZLUs2fPZpPcBoNB1113XZvbCgRCXFycampqVF9fr5UrV+rIkSOqrq7WvffeG+ymAWHPZDIpISHBEV8l1wtxktwId7169VKPHj2UkJCgWbNm+STBbVddXa2NGzdqzpw5WrBggU+2CbSX80juQCe5V69eraKiIsXFxen+++8P6L4BAJHFXn3nvffeU1lZmdfrU30HwdJSufIOHTo4HtunCwMQeUhyR4n169dr1apV2rJliwoLC2Wz2WQwGPTOO+9o5MiRmjhxosaMGRPsZsLPQjlx7fy4vYlrb4wZM0axsbFelWZua3mb2NhYvmcIebGxsY4kd0VFheOHTlGAbyQmJqq2trZJuXKTyUSFHUSMzz//XGvXrm0xwT1gwAA99thjOv/885WTk6Pq6mp9//33euaZZ7R27Vq361RXVys3N1cTJ05klBBCQjDLldtjhi+v2YBQFa3lN4FAsFqtVN9B2HK+R9u4IiFJbkSbaD1f4k5aBCspKdH999+vdevWqaGhwe2H/MSJE/r73/+uVatWKTY2VmPHjtW8efOUkZERhBajLTxNXNsf+ytxbU9Oh0Li2ltjx47V6tWr/b6fcePG+X0fQHvZR5I2NDSotrZWkpSQkED5G8BHkpKSdPLkSdXW1spisejaa69VTU0NUwQgYtTU1GjmzJmtzsHds2dPpaSk6PXXX9fRo0eVlJSk66+/Xrm5uZo6dapeffVVt+sVFxdrxowZysvLY95HBJ29XLnBYFDHjh0Dum97B0SLxeLoxA5EEj7TQGDcd999VN9B2HKuhuY8FZjkmuSuqKgIWJuAQOJ8iSR3xFq0aJEeffRRmc1mj5a32Wyqr6/X6tWrlZubq7lz52ratGl+biWaY09cFxcX6+jRo465cIKZuHaXxA7lxLU35s2bp9zcXI+/L20RExOjZ555xm/bB3zFfoFACWXAP5KSkhyPq6ur6ViIsJefn6+8vDx9/PHHys/PV0VFhY4dO9bqeh988IE++OADl+cWLlyoL7/8UnPmzGk2yS2dmi/yzTff1OTJk9vdfqA97Enu1NTUgFfjcL4Os1gsVANBxLBXIvz0009VVFQk6dTn/dFHH9XIkSM1YcIEKqQBPkL1HYQ75/tVjTuOJycnOx4zkhuIXFwFRRiz2axhw4bpxx9/bHN5ArPZrIceekhLly5VXl4eF8s+Yk9cO5cDt4+6bi5xbTabVV5e3u6bJp4kru2PIyVx7Y2MjAzNnTtXDz30kF/KehgMBj311FMkMhAW7KWerFarYyQ3SW7Ad5xHntbU1CglJSWIrQHapqamRsuXL9fixYtVUlKikpISWa3Wdm/XarXq0KFDuuCCC1pcrra2VosXLybJjaCqqalxnCsFulS5JJfrQ5LcCHetVSK0WCwqKCjQO++8o5UrV1KJEPABqu8g3OXn5+vTTz/VP/7xD508eVJxcXGaN2+eevfurdGjR+ucc85xLFtZWRnElgKBQblyhD2z2ayzzz5bx48fb/e2bDab9u7dq4EDB2rXrl1cMDejLYlrXzEYDC7zW5O4bp9p06Zp6dKl2rt3r8+33bdvX911110+3y7gD/aEtnPpy/j4+CC3Cogczr3JfVUSEAikrVu3asaMGSooKGhSErAtkpKSlJiYqI4dO2r8+PG66qqrtGLFilbXKykpUX5+vvr379/uNgBtYR/FLUnp6ekB37+9XLl06l4A52sIV1QiBIJj+fLlKigoaHU5qu8glDTubFtcXOyS2Dt8+LB27typ3NxcZWRkyGazaciQIerUqVMQWw34D+XKSXJHlGHDhvkkwe3s2LFjGj58uHbs2OHT7Yay1hLXjZPY/kxcx8XFqaqqSr169VJaWhqJaz/Ly8vTwIEDPSqx6amcnBxt27bNZ9sD/M0+ktt+kykmJoaR3IAPOY9iKC8v1zfffKO4uDilpaWpS5cuQWwZ0DKr1ar77rtPa9eubXXEjzfmzZvnSE5YLBatWrVKM2fObHW94uJibd++nSQ3AsZemn/Tpk06cOCAqqurVV5ervT0dNXU1Oj0008P6Oex8UhuINyYzWZdcMEFVCIEgmTx4sVt7rBI9R0Egzedba1Wq2PKi40bN+pf//qXBgwYQPl8IAJx9hchXnrpJf34448tLjNp0iTdf//96tevn+Lj4x3lniZPnqyPPvqo2fX27t2rRYsWhXXP2FBKXDf34y5xXVtbq0OHDqlHjx5KSEjwWZvgXkxMjHbt2qXhw4dr79697SrxYTAY1LdvX23bto0LbYQVe0LbbDbLYDAoJiaGkUGADznPyV1SUqK8vDxJp+a6u+aaa4LVLKBFVqtVkyZN0saNG31egWDBggV699131bVrV914440ymUweda6yWq3atGmT7rjjDp+2B3DmSWn+EydOaM+ePXrrrbeUkZGhKVOm6LbbbvN7adbGc3ID4cRsNuv888+nEiEQJPn5+SopKfFqHarvIFisVqtmzZrV5s62FotFJ0+e1G9+8xuNGzdO8+fPZ+AYEEE484sAJSUl+uMf/9hqQu6pp55Sly5d9O233+qbb75Rt27dNHr0aG3YsEHXXXed3nvvPbfr2Ww2Pfroo7rxxhtDaq4j58S1c2nwUEpcJycnKzExkcAZRmJiYrRjxw6vS6Y13gYl0xCunJPc9mMXI7kB33FOcpeVlTke8z1DKLvvvvv8kuCWTt1kzc/PlyS98cYb2rBhg3JzczV06NBW1/3666+1adMmxcfHKzEx0eXfhIQExw/n4mgLb0YL2Ww2FRYWqrCwUI888ogWLlyoF1980a+jhRjJjXB28803U4kQCKK8vDyvk4VU30EwWK1WTZ48WZ9++mm7r0VKSkq0cuVKFRcXa9myZVwjIOIwJzfC1v333+9RIu6JJ57Q8uXLXQLC5Zdfro8++kjz589vNsktnUp2PPDAA1q6dKlP2tyc5hLXzc137Y/EtSfJaxLXkW/atGm68cYb9cADDyg3N1cNDQ0tBgqDwaDY2FiNGzdOzzzzTEh1CAG84Vyu3D7XIyO5Ad9xHtlXWVnpeMz3DKHq888/19q1a1u8qTRgwAA99thjOv/885WTk6Pq6mp9//33euaZZ7R27Vqv9vfuu+9q8eLF6tevn/bs2dPislVVVdq+fXur24yPj2+S+G7801yinBGB0ae9pfnr6up08OBBTZo0SVdffbXfRgs5z8lNkhvhZPHixfr5559bXCaaKxECgbBx40avkyFU30EwPP300z5JcNtVV1dr48aNmjNnjhYsWOCTbQLBxJzcJLkjwrp16zxa7tVXX23y3CeffKLq6mplZ2e3un5ubq7XbZNCL3HdoUMHt0lsEtdoLCMjw9GxY/369Vq9erW2bNmikydPymazyWAwKD09Xeeee65uvvlmjR8/PsgtBtrPfpGakpKiX/ziF8rMzAxyi4DI4jySu6KiwvHY3sEECCU1NTWaOXNmq4m+nj17KiUlRa+//rqOHj2qpKQkXX/99crNzdXUqVPdXoc0x94RpGPHjq0u6+m5e11dnerq6lReXu5xO+xiY2ObHSHeWqI8JiaGmw5hxpel+YuLi/06Wmj06NEaNWqUYmJi6IyBsFFSUqInnngi6ioRAm1hsVhUX18vi8XS5KeqqkpHjx6VxWJRfHy8evfu7bLuvn37VFhYKLPZLIvFIqvV6nhsNpu1ceNGr9vTnuo7Bw4c8Hp/wLZt27R582afd7atrq7WypUrNXToUN1yyy3+aj6AAOFKKMytX79eDQ0N7dpGfHy8SktLW12uoaFB69ev15gxYzxKXDsnsAOZuHZ+TOIavjJmzBiNGTOmyfPO86YDkcCe5I6Pj9dpp52mfv366dChQ0FuFRA5mktyM5IboWj58uUqKChodbkPPvhAH3zwgctzCxcu1Jdffqk5c+a4TXJnZWWpsLDQ5bmYmBhNmjTJcXOqNeedd55uvfVW1dXVqaamxuXf2tpalx/7c95ORdPQ0KCGhgaXygueMplMbhPhniTK4+LiSJAHga9L8/tjtFB+fr7y8vK0adMmHThwQFarVUajUb1799bo0aM1bNgwSsIiZEVSJUJEBpvN5kj82hPIBoNBKSkpLssdOXJENTU1LsvZf5yf69mzp3r27OlYr7a2Vh988EGTZRsnna1Wq2666SaXQUjff/99s0k6s9ms8vJypaamqmPHjrr33ntdXv/uu+/03XffuV33559/VlVVVav/N60lD72pvkPFEXirpqZG9913X6s5i7Z2tq2qqtKMGTP03HPP6a677tJtt93mUnUNCEeUKw8jO3fu1D/+8Q99/fXX+uabb1RcXKwRI0Z4PKK5sa+++kpPP/20vvjiC5nNZg0YMEAzZszQdddd5+OW+96qVava9eF96aWXFBMTozVr1rS6rM1m0zPPPKMff/zRr4lre/KaxDWA9iJeeM95NGl7O1EBaCo2NlYxMTEym82qqqpyJLeZkzu4iBfuLV68uNW5iJtjtVp16NAhXXDBBW5ff+WVV5SamqrPPvtMR44cUU5Ojm677TadddZZmjNnTqs3X41Go6666iqXG8meaGhoaJL8bpwgby5R7m1ctI+08uRGcmNGo7HFpLi78ur2f+Pj47leagNPSvO78/DDD+upp57Srl27NGjQoCavV1dXKzc3VxMnTmzzHN01NTVavny5Fi9erJKSEpWUlMhqtboss3PnTuXm5iozM1MZGRmaMmUKN2z9gHjRPqFeiRCBU1FR4TZJ3Pi5bt26uVR3KSsr086dO5tNMjs/d+ONN7pUuvjiiy+0Y8eOJss21r17d/3mN79xee6jjz7yqOOf0Wh0OTex2Wz64YcfPPo/aXye4TwtRUvcdRxpbl2z2az333/fo3vJrSUPvam+4+nfEkmIF+3jz862dlarVXv37tUjjzyihQsX6sUXX2zzuRoQLHSMDtMk97p16/Tcc88pLi5Offr0adM8WXafffaZrr/+eiUkJGjChAnq0KGD1qxZozvvvFOHDx/WPffc48OW+96WLVvavO6YMWM0bdo0lZeX66677vJonfz8fJdRRy0xGo0uZcFbSlwnJSXxhQTgc8QL7zkn2urr64PYEiAyGQwGJSUlqby83CXJzUju4CJeNJWfn6+SkhKv1klKSlJiYqI6duyo8ePH66qrrtKKFSvcLrtixQpNnjxZ06dPV2ZmpioqKvTll1/q97//vUfJiczMTF100UVetU861dEkNja2yQgtT1gsliaJb08T5d52FrBaraqurm7TiGKDweBIdjuXWTeZTCovL9eRI0eUmpra7Dzk0Xgj2tPS/I1169ZNDz/8cKsj/YuLizVjxgzl5eV5nXTeunWrZsyYoYKCglY/R1arVYWFhSosLOSGrZ8QL9ouWJUIo5HzKGHnkcIWi0Xp6ekuid/S0lIVFBS4jDCurq5WQUGBDh06JJPJJIvFopiYGF1yySUu+9m2bZt++umnFkc2WywWnX322frVr37lsu7ixYs9ut689tprXZKoFRUV+vzzzz36fzCbzS5/a319vUeVWRp3IpI8nyKlcdK8uekkjEajYmJiZDKZHD+N74umpKTo9NNPl8lkUkxMjIxGo+OxxWJRSUmJcnJylJyc3GT7Q4YMUZ8+fZqsu2LFCtXU1Hj0t9iTh87Vd5yTh/bqnZ5U32lcTj0aEC/ax9NjhDutdbZtrK6uTgcPHtSkSZN09dVXa/78+XQYBcJIWCa5r732Wl111VU6++yzVVJS0uZSXGazWbNmzZLRaNS6det0zjnnSJIefPBBXX755XryySd1zTXX6LTTTvNl833q5MmTbVpv4MCBWrNmjSwWi0aNGuVx2b7a2lp16NChSeLaXRKbxDWAYCNeeM+e5K6qqlJBQYF69erl9iIfQNs5J7nt80Uykju4iBdN5eXleX0zbt68eZo2bZqkUzd5V61apZkzZ7pddsWKFc0mwD2RkZER8JLMJpPJcR3kLavV6lI23dtEuTfVu+xTS9XW1qqsrMzxvL206cGDB1ucwzkuLq7ZBLgn85CHI09HCzX27LPPavv27TKZTOrUqVOLyx47dkxvvvmmJk+e7NG2rVar7rvvPq1du7ZNN8a5YesfxIu2C3QlwtWrV/s9yW3/e5zvfTlP29dcaWqLxaLk5GT16dPHZXtffPGFysvLWy1rPWTIEA0cONCxXmVlpV599VXHci39P0+ePNllNPxPP/2k999/32UZ51LY9uN6hw4dmiS5CwsLdfDgwVb/n9wlqjztUOVp0tiTdRMTE5WamupIKjdOMtufS09Pb7KtwYMH64wzznBZrnGiOiYmRmlpaU3ae/fddzfZnyfH4x49euimm25y+5rz1HkJCQlNXu/SpYu6dOnS5Pm3337b68Rh4+o7CQkJ6tOnj2JiYjyuvjN69Giv9hkJiBdt5+/Ots0pLi7WypUrVVxcrGXLlnHehJC2fv16rVq1Sps3b3ZcKxiNRnXq1EkjR47UhAkToqazX1hegZ511lk+2c5nn32mAwcO6LbbbnMECOlUmZU5c+bo7rvv1ltvvaXf//73PtlfqOjevbu++OILmUwmXXPNNfr66689XtdkMkVk7zAAkYl44Z3169frf//3f7V582ZVVVXJYDDIYDAoIyNDl1xyiSZOnBg1J0iAP9lH8TU0NDhGmJDkDi7iRVObNm3yOhmxYMECvfvuu+ratatuvPFGmUwmv3y2ExISNHXqVJ9v15/sVa6SkpK8XtdmszVJjrc2/7hzotzbzmr19fWqr6/3uIKXs5iYmBZLqreUKI+NjQ1aJ+m2lOYfOXKkbrjhBp133nl64YUXWl2+trZWixcv9ijJbbVaNWnSJJ/MD84NW98iXrRdoCsRbtmyRd9++22TkcX2RLFz4tg+57DdsWPH9OGHHzYZEe1uXuUHH3zQJWG7Y8cObd++vdX2nXbaaU2S3N99952OHz/e6rqN1zMajaqtrW11Palp4retyWZ367pLGptMJnXo0KHJun379pXZbHZJFNv/dX7O+X2RpPT0dN10000uo5rdjXQ2mUwuU3FJ0i9+8Qv94he/8OjvbWzw4MFtWs9gMHhUzjsQ2pI4lKR//OMfuvPOOzVz5kxlZGTIaDTq2LFjuuuuu/xafSfcES/azt+dbVtSXV2tjRs3as6cOVqwYIHX6wP+VFJSovvvv1/r1q1TQ0NDk+t1i8WigoICvfPOO1q5cqViY2P161//WjNnzlSPHj2C1Gr/C8skt6/Yy9tcdtllTV67/PLLJZ0qDRZJUlNTtWvXLiUmJmrq1Klau3atV+tzQQwgGkVyvGjpBMn+uLCwUH//+9+1atUqxcbGauzYsZo3b55jBCoA79iTXPYbeDExMZQrjxCRFC8OHDjg9Tr5+fnKz8+XJL3xxhvasGGDcnNzNXToUJ+2LSsrS7feeqvj94aGBhUUFDjmvHf+iY2NdVsCNJwYDAZHQthbNpvNMQ95WVmZDhw4oPT0dEfivLV5yD2t+GVnNptVWVnpUTnYxkwmU5PkuLuEeHOJ8ra+x2256W80GvXCCy9oyZIl2rVrl8frlZSUKD8/v9XRXA8++KBPEtx23LANPZEULzwV6EqEhYWFmjt3rqxWq6PzruQ66tr+/A8//KCuXbs6fi8pKdG2bduarOPu33Xr1jk66RiNRn333Xfav3+/yzKN9ymdmiYhLy/P8brBYNChQ4dUVFTU7Dr25/fv36/09HTH7/aEe+MRyo1HGZtMJpWWlrrsMzY2Vueff75LYthisai4uFjdunVzVOmIi4tTTU2Ny7qjR4/W5Zdf7pKc9tTYsWM9XtZZfHy8Tj/99DatG+3akjiUpGHDhunSSy+VdCqB8u6772rq1KkeTR0gBaf6TiSJxnjhy862AwYM0GOPPebozGQvsf/MM880mxeprq5Wbm6uJk6cyJQvCBmLFi3So48+6vF5kM1mU319vf7xj38oNzdXjz32WMQOXo3qJPe+ffskSWeccUaT1zp37qwOHTpo//79Hm3L0x6Tvpaenu5xWbWYmBj98MMPSk1N1Z/+9CctWbKkTfsL1t8ajewlhJgXN3SF63vUlpuk0SwS4oU7r776qh5//HGvT5BWr16t3Nxc/elPf9KUKVP83Eq0JlyPQ9Gk8XsUExMjs9ms7t27a+LEierevbuk0Do+2BEvvBNJ8cLdiC1vvfvuu1q8eLH69eunPXv2+KBVpz6Ts2fPlsFgcPwfFRYW6vXXX292HYPB4LgB/1//9V8u83Hn5+fr22+/bZIcb/yTnJzcZEROUVGRoxqDc0I9NjZWRqMxpBLrcXFx6tixo7p06aLOnTt7PMLebDa7jBBvXHLd+Tl7otz+nLdxyWw2ez2a2s4+D7m7ZLj9cXPPOZf489S0adPUs2dPXXHFFV6tV1xcrC1btqhnz55uX6+vr9eXX36p999/3+sE98MPP6ynnnpKu3bt0qBBg5q8Xl1drTVr1uiaa67RsGHDvNp2a4gV3oukeOGptpQqb08lQqvVqqqqKo/i2U8//eTynauoqHBJNtsT2PZju/O/X375pUsJ7eLiYtXX1zuWabyu/aeiokIff/yxSztqamqUmJjYZB+N93vgwAG3ndGsVmur8557Ul7cYrGoqqpKycnJHo/0tnP+m53/XntMdPe8fXnn9duzncadAny9reaWb+7H19sxGAxqaGjQyZMnFRsb6+jo1drPhg0b2vQ9bE+lnvj4eP32t791HKeIF96Lxnjh6d/jrLnOto899phSUlL0+uuv6+jRo0pKStL111+v3NxcTZ06Va+++qrb7RUXF+vOO+/Uc889p5SUFMd0PnFxcW4fh3un2rbifpT/mc1mXXrppdq3b1+bp32xWCz64x//qNdff12bN28OiymmvIkXof/X+FF5ebmkU6Ob3UlJSXEs05qjR4/65EaQtwYPHuxxknvHjh3q0qWLjh07pi5duuill15yef3uu+9udRvnnnuuDh061Ka2ou08KVeF4Aqn98hkMtHz2UuREC+cmc1m3Xzzzfr555/bfIJkNpv1xz/+UUuWLNHbb78dFidIkS6cjkPR6vjx49qyZYvefvtt7dq1S3V1dfrrX/8qo9Gojh07asiQIbryyis1cuTIYDdVEvGiLSIpXng7gtcde2l+X5XpjImJUa9evdS1a1eXa5ITJ054/P9aUFDgMvIoPz9f33zzTavrZWZmNim3unbtWh09etTt8s6J9YEDB2rIkCGO16xWqzZs2NBktJ27kXc9e/Z0mQO8rq5OFRUVbpf1NBHRnnhhv5nn3FGgORaLRfX19Y7Ed2uP6+vrHcnxQN4o+/jjj706H8rIyNATTzyhJ598UkVFRV7ty2q1av369Ro1apTb12tra/Xkk096PbK8W7duevjhh1sdQV9SUqKZM2fq7bff9lmigVjRNpEUL/ylvZUIJalr164u3+/mEocmk8nl/9tms+nss8/2KGHRuENKbGysy5zXLWnuPbbZbF5PNeEPrc21jPCyY8eONq3Xnko9nTp10vDhw3Xo0CHiRRtFY7zwRTLe3tl23759uuqqq1xeW7hwob788kvNmTOn2SS3dKpD68svv6wBAwa0uj+j0ai4uDjFxsa6/BsXF+eohuH8XOPlnP8Nx2Q596P8w2w2a9y4cV5fc7hjs9n0448/6txzz1Vubm5I38f1Nl4E7S955JFHvLpwnTZtmtseS6Gia9euQdnvrbfe6nFPvN69e0uScnJyNH369Cavt5bkNhgMuvnmmyO6fn+oqa+v1/Hjx70abYHA4j3yP+KFb9nnnPPFCajNZtNPP/2ka6+9tskIBgQOx6HQd+LECd1///3avHmzzGazy3mb/SbDiRMntH79em3YsEExMTEaO3asnn76aaWnpwer2WGHeOFbffv2ddzQbE1WVpYKCwtdnouJidGkSZMcJQHbKyEhQRdccIGeeOIJdevWzeVGX2pqqi6//HKZzeYmPw0NDY75U81ms3r16uVyrPzpp5+avWnY+G9sfB2UmprqUVnuzMxMl3Xr6+tVVlbmyZ+tAQMGuKy7Z8+eJiP/7BpPgfC73/3O5fV//vOf+vbbb5WZmanExETHqHP7v/aEeXp6uk477TSXdYuLix2Je+cff01nZbVaXUaFuxsp7jyavPHocm+SQ94mcebOnauSkhKP5uF25/jx481eUy9ZsqTJd8kTzz77rLZv3y6TyaROnTq1uGxRUZG2bdumO+64w+v9RAriRXBkZGQEtBJh586d9de//tXta/ZzMavVKpvN1uTH/rx92cbPe/Pjq235ejuNt+X8fENDg0pLS5WamiqTydSubfmqTc6vo22j7X0Vsz2t1GMymfTiiy+qb9++PtlvMBAvgsMXHfFa6mxrtVp16NAhXXDBBS1uw2q1avfu3V7PKd/Q0KCGhoY2TTtjMBgcifDWRo83fuz8XFxcnNeVONqC+1H+dfHFF/skwe2sqKhIkyZN0pYtW3y63WAK2t3o1157zauLyfHjx/s8SNhvZjTX26miokJpaWkebStY5VbGjx+v2NhYjwJue2+SxsbGavz48e3aBtomLi6Okj4hjvfIf4gXvnXBBRf4vIfl8ePHddlll7W5Zzh8g+NQaGrLvEkNDQ36xz/+obVr12ru3LmaNm2an1sZGYgXvnXFFVdo3bp1Ht1QfuWVV5SamqrPPvtMR44cUU5Ojm677TadddZZmjNnTrtHgmVmZmr8+PGaN2+e2xu0CQkJGjNmTJu2PXr0aF188cWOZHhDQ4NLctz+u7s5sQcNGqTu3bs3Sao3TrSnp6e7rGuxWDzuGJacnOyyrn30dnOsVqujTG7j9hYVFenAgQMqLi5ucRtnnnmm+vXr5/Lc6tWrVVFR0WRZe3vsyfLRo0e7lHUvLy/Xxo0bXRLp7hLrsbGx6tu3b5ObYzabTUlJSY71YmJiWh3dYp9epbl5xhsnzb3ppNenTx9NnTpVs2fPdrlRnJCQoNjYWPXs2VPl5eUtzj1ss9ma/X6//vrrXo9iHzlypG644Qadd955HiXe6+rq9Le//U133XWXV/uJJMSL4Bg5cqTeeecdj5b1RSXCkSNHhs3/Taipra3VoUOH1KNHj5D6P/S0c0Jbkuvt7VTQXELeX22tr69XYWGhMjIyFBMT41EHAV+9l55W6unYsaNjLu9wRbwIjtNPP13ffvutR8t62tk2KSlJiYmJ6tixo8aPH6+rrrpKK1as8GgfF1xwgbp27eqoNtRcRSLnf9tTFctisaimpkY1NTVt3oYkl5HjjRPh8fHxbp9z95on59/cj/K9l156yTFdQXMmTZqk+++/X/369VN8fLwsFosKCgo0efJkffTRR82u9+OPP+q1116LmHtNQUtyHzlyJFi7drAHnX379uncc891ee348eOqrKx0KSsXqsaOHavVq1f7fT/jxo3z+z4AoDHihe+89NJL+vHHH1tcJjs7W++8844GDBigzMxMGY1GLVmypNW5t/fu3atFixZFzAkS0F5ms1nDhg3Tjz/+2OZRJ2azWQ899JCWLl2qvLw8qiW0gnjhW8OGDVNmZqZHPcdXrFihyZMna/r06crMzFRFRYW+/PJL/f73v1dubm6b25CQkKCcnBy9+OKLGjFiRJu30xJ7me+23JS58MIL27TPpKQkzZ49u9nEunOyvPFNy7S0NJ133nlul3VOtMfHxzfZr6c329wda5orbWmxWGSxWBxzaTfeR3V1tXbv3u3RfmfOnOmS5N65c6c2bdrktn3OP9nZ2br++usdrxsMBn355ZcqKSlxJMadf1JSUpSWlqbY2FhlZWVp4cKFzZadb6xbt24ymUx64YUX3CaUDx48qAULFui+++5rdhvNjarJz8/3uky50WjUCy+8oCVLlmjXrl0er1dSUqL8/Hz179/fq/1FCuJFcEyYMEErV6706LzIF5UIr7vuurY1FCHLnmQJxOjEUNeWjgjvv/++V1NQtrdST6hMwdQexIvgGD16tNasWePTzrbz5s1z3K+yWCxatWqVZs6c2er2S0tLdfz4ca871JrN5lYT4i0lye2P2zOFj31EeXs7HBuNxmYT4QaDQWVlZTp06JDL3OXx8fGKjY1tsp6/qkBFmpKSEv3xj39s9Tvw1FNPqUuXLvr222/1zTffqFu3bho9erQ2bNig6667Tu+9957b9Ww2mx599FHdeOONysjI8MefEFBRfadsxIgReu6557Rx40aXi2JJ+uSTTxzLhLp58+YpNzfXJ/PmNScmJkbPPPOM37YPAKEsEuKFpydIffv21ahRo2Q2m1VWVuZxFZBIO0EC2sNsNuvss8/22bQAe/fu1cCBA7Vr1y4S3SEuEuKFXf/+/ZWRkeFxktvTkRCtMRqNyszMVGZmpqZMmaJbb73VMWIoUhgMBiUmJrbp7+rWrZu6devWpv3+8pe/VJ8+fdS5c2fFxMS4TY67S6xLp8qm19bWui0F77ydxsl1b65RY2NjXX5vaGhwu5x9X3ZJSUlNltm3b58OHz7c6j6HDx+u3r17a+fOnR61cdeuXbr22mubPD937lylpKRo1qxZrY64yMrKavJcTU2NNm/erOLiYo/aYTdt2jT17NlTV1xxhVfrFRcXa/v27VGb5A4FkRQvPDVmzJiAViJsa4UPIFJ5kziU2lepx2Aw6LLLLvNV06NaNMYLf3S2XbBggd5991117dpVN954o0wmk0flta1Wq3Jzc3XJJZc4Osc6/xiNRiUnJzc5j7V3rnR3nuoNe5Wmuro6NTQ0eJ0kd/7Xm+l8GrehuZHlZrNZ5eXlOnjwoEf3KpxHlns6ktzdc5He2en+++/36DrqiSee0PLly11K419++eX66KOPNH/+/GaT3NKp9+6BBx7Q0qVLfdLmYIqKu2QNDQ06cOCAYmNjHb1BJWnUqFHq1auX3n33Xd11110655xzJEllZWV67rnnFBcXp5tvvjlYzfZYRkaG5s6dq4ceesgv89MYDAY99dRTJC0ARLxIjheeniB98803GjRokHbt2qXbb79db7zxhsf7iKQTJKA9hg0b5vNpAY4dO6bhw4czLUCIiOR44Wzq1Kl65JFHHKN0/cFgMCgnJ0fZ2dnq3bu3Ro8erYsuuojkmx8kJCSoQ4cOysjI8Hrk+i9/+cs27dM+CrOlUef21xrfHMzOztbZZ5/tdlnn7bj7W5obed6YvcS6pzf9i4uL3d4smj17tiS1eCPJzt0o+02bNulvf/tb6w12kpGRoSeeeEJPPvmk13P1Wa1Wbdq0Karn5Q6UaIkXnqISIRA83iQOpfZV6unUqZPX8xhHO+LFf/ijs21+fr7y8/MlSW+88YY2bNig3NxcDR06tNV1v/32W7399tvNvn7dddfpzDPPdPx++PBhLV++vEky3D7Nj/NzN910k0uyPT8/X3v37nWbTLc/tlcmGjx4sEs7Dh8+LLPZ7LKs8/o2m01ms1k2m81RicnTJLnzqPTmOqJ6wr6dysrKNm9DOtWJoKUkubsy7e6Wi42NbbUUezCsW7fOo+VeffXVJs998sknqq6uVnZ2dqvrt6fqWigJyyT3nj17NH/+fEmnyrNIp8qkOpcwevnllx2Pjx49qgsvvFA9evRwmc8hJiZGf/3rX3X99ddr7NixmjBhgjp06KA1a9bo0KFDevLJJ9WzZ88A/VXtM23aNC1dulR79+71+bb79u0b1fN1AQhfxIv/8PQEqbKy0qtyl41FygkS0FaeTAvQ1nmTmBbAf4gX7t12221auHChDh486Ld99OzZU3l5eRE3WhunxMTEeDxPZGP9+/dvc2eHCRMmqL6+3u2Ic+dkedeuXZWVleXVTf/2SEpKcrlxbWc2m1ucx9uduXPnqqSkxKN5uN05cOBAm9aLdsSL9qESIRA83iQOpfZV6snIyIj6DovEi/aZOnWqHn744XaV627Ju+++q8WLF6tfv37as2dPi8u2NgK68ahii8Uiq9Uqq9XaakK4cfnuY8eOeTQfeY8ePZp8xz7++GMVFBS0uu4ll1ziMvq/pqZGr7zyittkuslkUmxsrBISEmQymXTFFVeoY8eOjoT3vn379NlnnyktLU0mk8kxlZHzj9VqlcViUadOnVwS6GVlZWpoaJDBYJDRaHT5t/Hjxoloe2fX9pZiNxgM7Zqv3B+l2NevX9+ujgTSqU61paWlrS7X0NCg9evXh331m7BMch8/flxvvfWWy3MnTpxwec45SLTkkksu0fr16/X0009r9erVamho0IABA/T4449rwoQJPm23v+Xl5WngwIE6duyYz7aZk5Ojbdu2+Wx7ABBIxItTfHGC5KlIOUEC2oJ5k8IX8cK9xMREvfjii5o0aZLXZZQ9kZmZqZdeeokEN3wuNTXVq+W9uenvzujRoz1arlOnTm7Lnefk5HhUMtOuT58+mjp1qmbPnq2uXbs6nk9ISFBsbKx69uyp8vLyFhPnno52hyviRftkZGToT3/6k0fnS21BJUKgZYGo0pOQkKCpU6f6bfvhgnjRPrfddpteeOEF/fTTT37Zvv36o2PHjq0um5qaqhEjRjiStfbOkvYEbkpKisvyMTEx6tKli8tyzsle+4/NZmuSGPWmGlFjbV3XYrG4LUXuzqhRo2QymVymXjp69KgqKytbLFeenJysSZMmuTy3Zs0a7dy50/F/1Phf++MePXrovPPOcxldvnnzZkcJdvvf7S5J3rVrV5frgrq6Oh07dqzZhLrzv2lpaS7vj70DbXPrNTeC3NvE+d///vd2nSO99NJLiomJ0Zo1a1pd1mazafXq1WF/D9dQWlrq+7NKBI3ZbNbw4cO1d+/edn0ZDAaD+vbtq23btjH3YxDV1tbq0KFD6tGjh9clBREYvEcIB1OnTtU777zj9Xr2cuVLlizRlClTPF7vpptu0iuvvOL1/tA2HIdCx5133ulRCc4pU6Y0O2/SwYMHdfrpp7e4/vXXX8+0AAioWbNmaeXKlS6f2fZKSkrSjTfeqAULFvhsm2gZ8aJ5S5YsCchN/6eeekqTJ092+/oll1zi8dzgo0aN0ubNm1tcZsGCBbrvvvuafX3w4MH69NNPPdof4Eu1tbUaOnSoXxIX/fr1Y2oXHyBehL62vkc1NTUaNmyYX6v09OrViyo98IlNmzbpzjvv9GhEanOysrJUWFjo8lxMTIy2b9+us846S9nZ2a2OBr722mv12muvtbkNzbFarU2S3NXV1aqpqXFJhpvN5iYJ8uTkZPXq1ctl3a+++koVFRVNlm38M3jwYJdR4BUVFXrzzTfd7qdx4nzKlCnq1KmT4/cdO3Zo5cqVSk1NbTGHlJKSopkzZ7o8949//EO7d+9u9f/pnHPO0dixY12emz9/vqNCgiS3HRAsFotGjhyp7t27OxLkBQUF2rBhQ5PlnP+f7YYOHeoyndLPP/+sn3/+ucW2Go1GpaSkaNCgQS7P79mzR9XV1a2OWs/IyNDbb7/d5nLuY8aM0fvvv6+KigplZmZ6VDmna9eu+v7779u0v1BB9jLCxMTEaMeOHVq0aJEeffTRNpWAiomJ0dy5cymFCQARYsuWLQHd32effSar1eq2pBAQyZg3CZFq/vz5Ki4u1saNG32S6E5KStJll12m5557zgetA9ovEKX5c3JydOuttzb7eu/evT1Ocu/atcvtiPC5c+cqJSVFs2bN0r59+1rchruy6UCgvP3227r22mt1/Phxn22TSoRA66jSg3AybNgwXXrppfrwww/bfA3yyiuvKDU1VZ999pmOHDminJwc3XbbbTrrrLM0Z86cVhPcRqPR44o93nJX3jopKUlJSUlt2t6QIUPatF5KSkqzU9XabDaXBHJ8fLzL62eccYauvfZade7cWTExMc2WK3c38rx3795KTk5usmzj5LO76izx8fEyGAwuiXij0eiSlJaaTn906NChFqfssc9XbjabNXnyZNlsNsfo8by8PJeS7I1HnNsfm0wmGQwGlwGo1dXVHiWu4+PjPR5V39jAgQO1Zs0aWSwWjRo1yuO8oLdTJoUiktwRatq0abrxxhv1wAMPKDc3Vw0NDS2O7DYYDIqNjdW4ceP0zDPPUNoJACJIoE9YCgsL9Ze//MXxu7uyP869Fd0t09zyLb3mzfItzfPj7fKtlThqbtveLt/cev4o9QjvMW8SIpnRaNSyZcs0Z84c5ebmtuumaGZmpsaPH6958+b5bN4yoL1C4ab/6NGjtWbNGo/ienFxsdupLWbPni1JzU57YefPG7aAJ2JiYvTll1/qsssuoxIhEGAjRozQ1Vdf7ZcqPePGjdPw4cN9tk3goYceUl1dnT799NM2fV5XrFihyZMna/r06crMzFRFRYW+/PJL/f73v/eo83hmZqYuuuiitjQ9IhgMBscc3e4kJycrOztb3bt397ryx+DBg9vcrrvvvtvl98bJeHvSuXGHgaysLN1yyy1uk9ONR7F36dLF5XrVYDAoOzu7ybLOpestFouys7N19dVXq6GhwTGCfPny5SooKFBDQ4PbEeT2x507d27TYKHu3bvriy++kMlk0jXXXKOvv/7a43Uj4Z4iZ38RLCMjw1HKcv369Vq9erW2bNmikydPOj68qampuuSSS3TDDTdwoxQA4BONT5Ds5X6Y+9E/zGazKioq1LFjR8XGxra7M4F92VDtHBDsjhDNWbVqFfMmIaIZjUYtWLBAEydO1IwZM3Ts2DGXEnGtSUhIUE5Ojl588UWNGDHCjy0F2ibYN/2HDRumjIwMvyTZG4v2G7YIDVQiBIKHKj0IF0ajUUuXLtXDDz/cps62K1as0IoVK9q8/4yMDJeRwAhNrSXj7RISEpqUeffUmWeeqTPPPNPj5e3zc0vSvffeK+nU/dGWysknJSVp0aJFXp0TpaamateuXUpMTNTUqVO1du1ar/6uSKjASZI7SowZM6bJjVDm2AEA+IPRaFT37t1ls9kcP1ar1eXf5p5r7jX7v3Cvca9V+EdLSfEPPvigzdsdM2aMpk2bpvLy8mbLhDUW6GkIALsRI0YoLy9Pb775phYvXqySkhIVFxe7zF9mZzQalZmZqczMTE2ZMkW33nor5SsR0oJ5079///7tTnJ7OjqbG7YIJVQiBAKPKj0IJ+3tbNtWCQkJmjp1qt/3g+hhNBplNBpbrDyTnp6ugoICj7YXExOjH374QampqfrTn/6kJUuWeN2m9PR0r9cJNSS5AQCIcN6cIPlCVlaWfvOb3/hl254mzO3JlpYS5u6Wby0J783yniTvfbF8fX294uPjlZ6eLpPJ1Op6dCZoO/v/m9S0MkFbkyHRPG8SwldiYqImT56syZMnKz8/X9u3b9cnn3yi/Px8xcTEKCYmRr1799bo0aN10UUXkUx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OOussSU17Am3fvl1ms1mdOnVy9KzJy8tzKTdRVlbm6I3lTfkLSTpx4oSk1g+4GRkZTZ6zzydx5ZVXNjlo2plMJj377LOKj49XRUVFk/93AE358vi5bt06HTlyRJJ0zz33uFxU2GVkZDhOPq1Wq5YsWdKufXqic+fOmjdvnttyVNOnT3ccK7du3drmfdhsNsffdeeddza5qLBLTEzUvHnzJJ0qmeVcdqixiy++2OWiwluBjI0S8ZH4iEhHvCBe+ArxgniByEa8IF74CvGCeIHIRawgVvgKsYJY0RYkuRFy7AchSS7lHvxh06ZNkqRevXrpkksuaXa5O+64o8k6duvXr5d0qrTItdde26721NbW6vbbb9ebb74pSfo//+f/6JlnnpHBYGh2nREjRkhyna9B+s+BdMSIEbrooosUFxen0tJS7dy507GMfX4IyfsDZ05OjqRTf79zj7TWlJeXO9p2zTXXtLhsWlqaIzDs2LHDq/YB0ciXx0/nY92kSZOaXW748OHq169fk3X8Zfz48UpISHD7Wmpqqvr06SNJOnjwYJv38cMPP2j//v2SWj9OnXnmmY4TxJaOUzfeeGOb2yMFNjZKxEfiIyId8YJ44SvEC+IFIhvxgnjhK8QL4gUiF7GCWOErxApiRVtQrhwhp0OHDo7H1dXVfttPXV2d9u3bJ0lue4U569y5s0477TT9/PPP+v777x3Pm81mx+9Dhw5125vLU5WVlbr++uu1detWGY1G/eUvf/FobgP7XA/l5eXauXOnY64G+8Hp4osvVlJSkoYMGaLt27fr888/d/RgauscD5J06623auvWrdq/f7/OO+88XX311br00kt10UUX6bTTTmt2vZ07dzoO8DNmzNCMGTM82p+9NxKA5vny+Ll7925JUpcuXVrt2fiLX/xCe/bs0aFDh1RRUaGUlJR27bsl/fv3b/F1e6kc5xNxb/373/92PB43bpzH67V0nBo0aJDb5w8ePNjse5WWluaYmylQsVEiPjovQ3xEpCJeEC98gXhBvEDkI14QL3yBeEG8QGQjVhArfIFYQaxoK0ZyI+Q4l0woLCz0235KS0sdjzt16tTq8p07d5YknTx50vFcSUmJo3eN/fW2+uabbxwlTX7zm994dNCU/jPXgyRHeRLnOR7svX7s/zqXymjrHA+SdNttt+nBBx9UbGysysvL9eabb2rq1Kk655xzNGjQID3wwAMuQcauqKjIq/3Y+TuQApHAl8dP+7HOm+Oj83r+kpiY2OLr9hNY596S3vLHcaq5eWpmzJih4cOHu/158sknHcsFKjZKxEfnx8RHRCriBfHCF4gXxAtEPuIF8cIXiBfEC0Q2YgWxwheIFcSKtmIkN0LOgAEDZDKZZLFY9PXXXwdkny2VmAiUs846S2azWXv37tUbb7yh4cOH66abbmp1PftcD99//70+//xz3XvvvY45HrKyshwlJEaOHKlnn33WUfaioqKizXM82D388MOaNGmS/v73v+vTTz/Vjh07VFlZqUOHDunVV1/VkiVL9OCDD+qhhx5yrOMc7P/yl79o5MiRHu0rECVRgHDnj+NnKBwfA835OLVs2TJH2anWNHfxIKldvUel4MRGKTTef+Jjy4iPaAvihW8QL/4jFN5/4kXLiBdoC+KFbxAv/iMU3n/iRcuIF/AWscI3iBX/EQrvP7GiZaEUK0hyI+SkpqZq0KBB+vrrr/Xjjz9q3759OuOMM3y+H+cA4ElPpOPHj0uS0tPTHc+lp6fLaDTKarU6Xm+rjIwMLV26VOPGjdPevXt19913y2AweDR3xsUXX6zvv/9eeXl5MpvNLnM82F144YWKi4tzlMooKCho8xwPzrp3765Zs2Zp1qxZjoCXm5urv/3tbyovL9df/vIXDR48WL/+9a8lnTrQ2yUmJmrAgAFt3jcAV748ftqPdd4cH53XszMYDLLZbI7jTXNCqQeg83EqNTXVr8epdevWebRcoGKjRHwkPiIaEC98g3iR5nhMvCBeIDIRL3yDeJHmeEy8IF4g8hArfINYkeZ4TKwgVniDcuUISbfddpskyWazadGiRX7ZR3x8vOOg/OWXX7a47IkTJ/Tzzz9LkssXPTY21vH79u3bWw2crcnJyVFubq769Okji8Wi6dOna+XKla2uZz/wVVRU6Ouvv3YcOJ173iQmJur888+XdKpURnvmeGiOyWTS+eefr8cee0zvvvuu4/l//OMfjscDBw509MbKy8vzyX4B/Ievjp/2noUFBQU6cuRIi8vaj6E9evRoMgeS/XfnskPu7N27t40t9ZynPUHPOeccx+NQOk4FIjZKxEfiI6IF8aJ5xAvPEC+IF4gOxIvmES88Q7wgXiDyESuaR6zwDLGCWNFWJLkRkm6//Xbl5ORIkpYuXapPP/3U43Xfe+89VVVVebTs6NGjJUn79+93zLHgzrJly5qsYzdmzBhJp4Lv6tWrPW5ncxofPKdNm+ZyEHLHea6H9evXN5njwXk56dT8Du2Z48ETF154oaNsRXFxseP5Tp06aejQoZJOvVdHjx71+b6BaOar46fzse5///d/m11n+/btys/Pb7KOXc+ePSVJ//73v5vdxq5du9zOC+NrCQkJjsf19fXNLnfOOeeoe/fukqQ33nhDlZWVfm+bJwIVGyXio0R8ROQjXjSPeEG8IF4A/0G8aB7xgnhBvABOIVY0j1hBrCBW+BdJboSkxMRELV68WCaTSVarVbfccotWrVrV4jpFRUX67//+b91xxx1qaGjwaD+TJ092zE0xZ84ct727vv76a82fP1/SqYPaNddc4/L61KlT1aFDB0nSAw884DhouXP48GGP2tWlSxfl5ubqjDPOkMVi0V133dXi35+RkeHopbR48WKZzWZlZ2erf//+LsvZD5xbt25t9xwPb7/9dov/z3l5eY6SL/YTC7sHHnhA0qmSML/5zW9cDqyNWSwWrVixotXeewBO8dXxc+zYserWrZsk6a9//avbuXdKS0s1e/ZsSad6pv7ud79rsoy9FM+//vUvtyeopaWlmjFjhjd/Ypt17tzZ8fjAgQPNLmc0GnX//fdLko4eParJkye3WMKqtrZWixcvVm1tre8a60agYqNEfHR+zlvER4QL4kXziBfEC+IF8B/Ei+YRL4gXxAvgFGJF84gVxApihX8xJzdC1iWXXKKFCxdq1qxZqq6u1m9/+1stXLhQ11xzjc455xylp6erqqpKhw4d0saNG/X+++973cPprLPO0uzZs/Xcc88pPz9fI0eO1OzZs3Xeeeeprq5OGzdu1Isvvqjq6moZDAY9//zzio+Pd9lGdna25s+frylTpqikpERXXnmlbr/9dl155ZXKyclRbW2t9uzZo48//ljvv/++R3NKSKcOnmvXrtXVV1+tffv2acqUKTIYDLruuuvcLn/xxRfru+++U3l5ueP3xoYOHar4+HiX/6e2HjinTZumP/zhDxo7dqyGDh2q008/XQkJCSouLta2bdv06quvSpJiYmJ0xx13uKx7+eWXa+bMmVq4cKG+/PJLXXjhhbrzzjs1YsQIZWZmqqamRj/99JN27Nih3NxcHT9+XNu2bXOc5ABomS+On7GxsXr++ec1ceJEVVVVaezYsZo+fbouv/xyxcfH69///rcWLFigQ4cOSZLuuecel9JKdnfeeaeWLl2qhoYG3XLLLXrggQc0YsQIWSwWffnll3rppZdUWFiowYMHt3ji6Qs9evRQt27ddOTIEb3wwgvq2rWr+vbtK5PJJEnKyspylLT6r//6L23evFnvvfeeNmzY4DhOXXDBBUpLS1NVVZX27dunvLw8rV27VmVlZbr11lv92n4pMLFRIj42t5wniI8IJ8QL94gXniNeEC8QHYgX7hEvPEe8IF4g8hEr3CNWeI5YQaxoC5LcCGm33HKLevfurQcffFA7d+7UV199pa+++qrZ5TMzM/X73/9eqampHu/jD3/4g6qrq7Vo0SIdOnTI0WPKWUJCgp5//nn96le/cruNiRMnSpJmz56tqqoqLV26VEuXLvW4Dc2x9xK6+uqrtX//fsfB89prr22y7MUXX6xXXnnF5Xd3f8f555+vbdu2SWr/HA+FhYV67bXX9Nprr7l9PTExUc8//7wGDx7c5LW5c+cqIyNDf/7zn1VcXKxnn31Wzz77rNvtxMXFuZR2AdA6Xxw/r7jiCi1evFj33HOPqqqqmv2eTpkyRY899pjb7fbv319z587V73//e5WXl+sPf/iDy+tJSUlavHix3n//fb9fWEineoLef//9+umnn5pcCLz44ouOuYYMBoOWLl2qnJwcvfrqqzp8+LCefPLJZrebnJzsuEDxt0DERon4SHxEtCBeuEe88BzxgniB6EC8cI944TniBfECkY9Y4R6xwnPECmKFt0hyI+RddNFF+vTTT/XRRx9pw4YN2r59u44fP67S0lIlJSWpS5cuOvfcc/WrX/1KY8eObdJ7pzUGg0F//vOfdf3112vp0qXatm2bTpw4oZiYGPXo0UOjR4/WtGnTdNppp7W4nYkTJ+rSSy/Vq6++qk8++UT79+9XeXm5kpOTdfrpp2v48OGOA6w3unbt6ugltH//fv3ud7+TwWBoUorDPteDzWZz/O7OyJEjHQfO9szxkJeXp48++kh5eXk6ePCgTpw4obKyMiUlJemMM87QpZdeqt/+9rfq0aNHs9uYM2eOJk6cqNdee02bN2/WgQMHVF5eroSEBOXk5Ojss8/WpZdeqvHjxyszM7NN7QSimS+OnxMnTtSwYcO0aNEibdy4UYcOHVJ9fb2ys7M1fPhw/fa3v3XM3dKcu+66S2eeeaZefPFF/etf/1JlZaWys7M1evRo3XPPPerXr5/ef/99f/03uJg8ebKysrL02muv6dtvv9XJkydlNpvdLhsTE6O//OUvuvPOO/X666/r888/16FDh1RRUaHk5GR169ZNgwYN0mWXXaaxY8cqMTExIH+D5P/YKBEfiY+IJsSLpogXniN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      "text/plain": [
       "<Figure size 2000x800 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sequence_pred(sbs_seq_transf, full_test, test_directions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wrS6ODOQNwUI"
   },
   "source": [
    "## The PyTorch Transformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KEKEUT90NwUI"
   },
   "source": [
    "![](https://github.com/dvgodoy/PyTorchStepByStep/blob/master/images/sublayer.png?raw=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "SudS3VazNwUI"
   },
   "outputs": [],
   "source": [
    "def encode_decode(self, source, target, source_mask=None, target_mask=None):\n",
    "    # Projections\n",
    "    src = self.preprocess(source)\n",
    "    tgt = self.preprocess(target)\n",
    "\n",
    "    out = self.transf(src, tgt, \n",
    "                      src_key_padding_mask=source_mask, \n",
    "                      tgt_mask=target_mask)\n",
    "\n",
    "    # Linear\n",
    "    out = self.linear(out) # N, L, F\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "mT4DINoSNwUJ"
   },
   "outputs": [],
   "source": [
    "class TransformerModel(nn.Module):\n",
    "    def __init__(self, transformer, input_len, target_len, n_features):\n",
    "        super().__init__()\n",
    "        self.transf = transformer\n",
    "        self.input_len = input_len\n",
    "        self.target_len = target_len\n",
    "        self.trg_masks = self.transf.generate_square_subsequent_mask(self.target_len)\n",
    "        self.n_features = n_features\n",
    "        self.proj = nn.Linear(n_features, self.transf.d_model)\n",
    "        self.linear = nn.Linear(self.transf.d_model, n_features)\n",
    "        \n",
    "        max_len = max(self.input_len, self.target_len)\n",
    "        self.pe = PositionalEncoding(max_len, self.transf.d_model)\n",
    "        self.norm = nn.LayerNorm(self.transf.d_model)\n",
    "                \n",
    "    def preprocess(self, seq):\n",
    "        seq_proj = self.proj(seq)\n",
    "        seq_enc = self.pe(seq_proj)\n",
    "        return self.norm(seq_enc)\n",
    "    \n",
    "    def encode_decode(self, source, target, source_mask=None, target_mask=None):\n",
    "        # Projections\n",
    "        src = self.preprocess(source)\n",
    "        tgt = self.preprocess(target)\n",
    "\n",
    "        out = self.transf(src, tgt, \n",
    "                          src_key_padding_mask=source_mask, \n",
    "                          tgt_mask=target_mask)\n",
    "\n",
    "        # Linear\n",
    "        out = self.linear(out) # N, L, F\n",
    "        return out\n",
    "        \n",
    "    def predict(self, source_seq, source_mask=None):\n",
    "        inputs = source_seq[:, -1:]\n",
    "        for i in range(self.target_len):\n",
    "            out = self.encode_decode(source_seq, inputs, \n",
    "                                     source_mask=source_mask,\n",
    "                                     target_mask=self.trg_masks[:i+1, :i+1])\n",
    "            out = torch.cat([inputs, out[:, -1:, :]], dim=-2)\n",
    "            inputs = out.detach()\n",
    "        outputs = out[:, 1:, :]\n",
    "        return outputs\n",
    "        \n",
    "    def forward(self, X, source_mask=None):\n",
    "        self.trg_masks = self.trg_masks.type_as(X)\n",
    "        source_seq = X[:, :self.input_len, :]\n",
    "        \n",
    "        if self.training:            \n",
    "            shifted_target_seq = X[:, self.input_len-1:-1, :]\n",
    "            outputs = self.encode_decode(source_seq, shifted_target_seq, \n",
    "                                         source_mask=source_mask, \n",
    "                                         target_mask=self.trg_masks)\n",
    "        else:\n",
    "            outputs = self.predict(source_seq, source_mask)\n",
    "            \n",
    "        return outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S4R3-nGxNwUJ"
   },
   "source": [
    "### Model Configuration & Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "Kq1DO_7kNwUK"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dvgodoy/anaconda3/envs/pyt20/lib/python3.9/site-packages/torch/nn/modules/transformer.py:286: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.num_heads is odd\n",
      "  warnings.warn(f\"enable_nested_tensor is True, but self.use_nested_tensor is False because {why_not_sparsity_fast_path}\")\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "transformer = nn.Transformer(d_model=6, nhead=3, \n",
    "                             num_encoder_layers=1, num_decoder_layers=1,\n",
    "                             dim_feedforward=20, dropout=0.1, batch_first=True)\n",
    "model_transformer = TransformerModel(transformer, input_len=2, target_len=2, n_features=2)\n",
    "loss = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(model_transformer.parameters(), lr=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "FT_3rWblNwUK"
   },
   "outputs": [],
   "source": [
    "for p in model_transformer.parameters():\n",
    "    if p.dim() > 1:\n",
    "        nn.init.xavier_uniform_(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "2h8ZHESHNwUK"
   },
   "outputs": [],
   "source": [
    "sbs_seq_transformer = StepByStep(model_transformer, loss, optimizer)\n",
    "sbs_seq_transformer.set_loaders(train_loader, test_loader)\n",
    "sbs_seq_transformer.train(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "id": "xqQkl4IrNwUK",
    "outputId": "0da6f28b-d24a-4c06-e6ac-44429e845bf7",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sbs_seq_transformer.plot_losses()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cSr-hYK1NwUL"
   },
   "source": [
    "### Visualizing Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 450
    },
    "id": "F34CjIPMNwUM",
    "outputId": "f5286617-7b42-475d-929b-e17531ca5ad6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x800 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sequence_pred(sbs_seq_transformer, full_test, test_directions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W1nna3PdNwUM"
   },
   "source": [
    "## Vision Transformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QYI3KD0JNwUM"
   },
   "source": [
    "### Data Generation & Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "cBpa0M2kNwUM"
   },
   "outputs": [],
   "source": [
    "images, labels = generate_dataset(img_size=12, n_images=1000, binary=False, seed=17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "qc3DcJ3rNwUN"
   },
   "outputs": [],
   "source": [
    "img = torch.as_tensor(images[2]).unsqueeze(0).float()/255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "x6dXu-7TNwUN",
    "outputId": "0b1baddb-cc0a-403a-c331-e931bd002295"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_images(img, title=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "z3sLRmR3NwUN"
   },
   "outputs": [],
   "source": [
    "class TransformedTensorDataset(Dataset):\n",
    "    def __init__(self, x, y, transform=None):\n",
    "        self.x = x\n",
    "        self.y = y\n",
    "        self.transform = transform\n",
    "                \n",
    "    def __getitem__(self, index):\n",
    "        x = self.x[index]\n",
    "        if self.transform:\n",
    "            x = self.transform(x)\n",
    "            \n",
    "        return x, self.y[index]\n",
    "            \n",
    "    def __len__(self):\n",
    "        return len(self.x)\n",
    "\n",
    "# Builds tensors from numpy arrays BEFORE split\n",
    "# Modifies the scale of pixel values from [0, 255] to [0, 1]\n",
    "x_tensor = torch.as_tensor(images / 255).float()\n",
    "y_tensor = torch.as_tensor(labels).long()\n",
    "\n",
    "# Uses index_splitter to generate indices for training and\n",
    "# validation sets\n",
    "train_idx, val_idx = index_splitter(len(x_tensor), [80, 20])\n",
    "# Uses indices to perform the split\n",
    "x_train_tensor = x_tensor[train_idx]\n",
    "y_train_tensor = y_tensor[train_idx]\n",
    "x_val_tensor = x_tensor[val_idx]\n",
    "y_val_tensor = y_tensor[val_idx]\n",
    "\n",
    "# We're not doing any data augmentation now\n",
    "train_composer = Compose([Normalize(mean=(.5,), std=(.5,))])\n",
    "val_composer = Compose([Normalize(mean=(.5,), std=(.5,))])\n",
    "\n",
    "# Uses custom dataset to apply composed transforms to each set\n",
    "train_dataset = TransformedTensorDataset(x_train_tensor, y_train_tensor, transform=train_composer)\n",
    "val_dataset = TransformedTensorDataset(x_val_tensor, y_val_tensor, transform=val_composer)\n",
    "\n",
    "# Builds a weighted random sampler to handle imbalanced classes\n",
    "sampler = make_balanced_sampler(y_train_tensor)\n",
    "\n",
    "# Uses sampler in the training set to get a balanced data loader\n",
    "train_loader = DataLoader(dataset=train_dataset, batch_size=16, sampler=sampler)\n",
    "val_loader = DataLoader(dataset=val_dataset, batch_size=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eHqOWqX4NwUO"
   },
   "source": [
    "### Patches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zKVO_YStNwUO"
   },
   "source": [
    "#### Rearranging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "SDxnlTwcNwUO"
   },
   "outputs": [],
   "source": [
    "# Adapted from https://discuss.pytorch.org/t/tf-extract-image-patches-in-pytorch/43837 \n",
    "def extract_image_patches(x, kernel_size, stride=1):\n",
    "    # Extract patches\n",
    "    patches = x.unfold(2, kernel_size, stride)\n",
    "    patches = patches.unfold(3, kernel_size, stride)\n",
    "    patches = patches.permute(0, 2, 3, 1, 4, 5).contiguous()\n",
    "    \n",
    "    return patches.view(x.shape[0], patches.shape[1], patches.shape[2], -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7UrluxHRNwUO",
    "outputId": "d993963e-9c97-4e92-a274-2b1ab591b0c4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 3, 3, 16])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kernel_size = 4\n",
    "patches = extract_image_patches(img, kernel_size, stride=kernel_size)\n",
    "patches.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "iXtVbwyJNwUP",
    "outputId": "60af95f9-1dc6-43f4-a117-5e9527cff272"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 300x300 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_patches(patches, kernel_size=kernel_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "skMZT66YNwUP"
   },
   "outputs": [],
   "source": [
    "seq_patches = patches.view(-1, patches.size(-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 309
    },
    "id": "Wv4MDoIaNwUP",
    "outputId": "1a4f2285-757b-454a-b64e-0017fd36124d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 350x400 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_seq_patches(seq_patches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "wRmNuEMVNwUP"
   },
   "outputs": [],
   "source": [
    "# Adapted from https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/vit_pytorch.py\n",
    "# !pip install einops\n",
    "# from einops import rearrange\n",
    "# patches = rearrange(img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', \n",
    "#                     p1 = kernel_size, p2 = kernel_size)\n",
    "# patches.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5mtLFtYjNwUP"
   },
   "source": [
    "#### Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "id": "Kk1XD8GlNwUP"
   },
   "outputs": [],
   "source": [
    "# Adapted from https://amaarora.github.io/2021/01/18/ViT.html\n",
    "class PatchEmbed(nn.Module):\n",
    "    def __init__(self, img_size=224, patch_size=16, in_channels=3, embed_dim=768, dilation=1):\n",
    "        super().__init__()        \n",
    "        num_patches = (img_size // patch_size) * (img_size // patch_size)\n",
    "        self.img_size = img_size\n",
    "        self.patch_size = patch_size\n",
    "        self.num_patches = num_patches\n",
    "        self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.proj(x).flatten(2).transpose(1, 2)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "k2AvFeBvNwUQ",
    "outputId": "80862bb6-f9b7-45e9-c638-24d112f6ba0d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 9, 16])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(13)\n",
    "patch_embed = PatchEmbed(img.size(-1), kernel_size, 1, kernel_size**2)\n",
    "embedded = patch_embed(img)\n",
    "embedded.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 309
    },
    "id": "OY3ZcArZNwUR",
    "outputId": "e87d67ae-28dc-4a5f-d907-bca36a072220"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 350x400 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_seq_patches(embedded[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WfFWwJiHNwUS"
   },
   "source": [
    "### Special Classifier Token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "36fTjAcpNwUS"
   },
   "outputs": [],
   "source": [
    "imgs = torch.as_tensor(images[2:4]).float()/255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 223
    },
    "id": "_bnPbXlNNwUT",
    "outputId": "c73437a9-a75d-4a4c-c1e5-9cbb1ccde397"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_images(imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "id": "ZBPAYtSXNwUT"
   },
   "outputs": [],
   "source": [
    "embeddeds = patch_embed(imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 436
    },
    "id": "4U9NfRw8NwUT",
    "outputId": "cb165f10-b029-402a-d463-dc586f08fa9f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dvgodoy/projects/PyTorchStepByStep/plots/chapter10.py:86: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  axs[i+1].set_yticklabels([label], rotation=0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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6vuZ+KtmCvjf19fULfFWuffv2mTVrVj7++OMmj56Z//wRSSo5+/fv36yci2ruK8Ivv/zyQl1/SeTr3bt3vv3tb1f+ffvtt6ehoWGex8bNN9+cLl26zPMqblOF7Gtf+1qSZMyYMU2uNW7cuEycODFJssEGGxSSH4Dqpjd9Pr1p4S1tvQmWBs7RBAWZ+4dyU6+CNTQ05Oabb27ydltuuWWSLHD7gi6f+971W265pdlZF+S2225LmzZtMmjQoGbdbkGvNn7W3KNZOnbsWHmVbcCAAenZs2defPHF/Otf/1ro9bbaaqskn37tnz0Z5Fw33njjAm87t2w1VUaef/75vPnmm40u32677dKhQ4c8+OCD+b//+7+FzrmodtpppyTJX//61899u9lcSyLfWmutlcsvvzyXX355zjnnnEybNi1bb7115bKf//znmTVrVnbZZZfKZZdffnmGDRvW6L522WWXdOjQIRMmTMijjz7aaPtf/vKXJMmmm25a2T8ALN30ps+nNy28pa03wdLAoAkKMvcViREjRsxzssvZs2fn9NNPz2OPPdbk7Y444oi0adMmf/3rXxsdOjtixIjKyTDn91//9V9ZddVV87e//S2//OUvm/y0rnfffTfXXnvtQuV/77338thjj2WzzTbL8ssvv1C3mev555/PoEGDctttt2XmzJmNtj/33HP56U9/muTTk2XOfd99+/btc+KJJ6ahoSHf/e5388gjjzS67ezZs3P//ffniSeeqFy21157ZaWVVsqECRPy61//ep7Dtl944YWcd955C8w6973yZ599dmbMmFG5/PXXX8/QoUObLH/LL798jjjiiEyZMiUHHHBA/vnPfza6zowZM3LHHXcs9Ktpn+eb3/xmNthgg7z99tv5r//6r0bnrpg+fXruueeeLy3fQw89lE8++STbbLNN5bIHHnggSbLtttt+4e3r6uoqHzd9wgknzPP1PP3007n44osr2wBoHfQmvUlvgqWXt85BQXbfffdsuOGGefrpp7PJJptkq622SpcuXfLkk0/mnXfeyXHHHZeLLrqo0e023HDDnHzyyTnllFNy4IEHZtNNN03fvn3z6quv5n//93/zwx/+MJdeeuk8H8ObfHrizZtuuin7779/Lr744lxzzTVZd911s/LKK2fq1KkZO3ZsXnrppfTq1SuHHHLIF+a//fbbM2fOnGZ/akry6StzDz74YB588MF07do1G2ywQVZcccXMnDkzr7/+ep577rkkyfrrr9/ofepHHHFEJkyYkN/85jfZfffds/baa6dfv37p3Llz3n333Tz33HOZNGlSLrjggmy66aZJks6dO+fKK6/Mfvvtl0svvTQjR47MgAED8uGHH+bBBx/MbrvtlqeffrrJw7mPP/743Hrrrbn77ruz8cYbZ8CAAXn//fczZsyYbLbZZtlss82aLLe/+tWv8u677+Yvf/lLttlmm6y33nrp27dv2rVrl7feeivPPfdcpkyZkptvvnmxD4Nu06ZN/vSnP2WfffbJPffck/XWWy+bb755ll122bz11lt5/vnn071798r3dUnnGz16dJI0WZgW9oSW//3f/52nnnoqjz/+eAYMGJBtt902U6dOzf33359Zs2blqKOOym677dasXAC0XHqT3qQ3Ldh1112X6667rvLvd999N0kybNiwysn1e/funeuvv75Z2eDLYtAEBWnXrl1uv/32XHjhhRkxYkTuv//+LLPMMvn617+eP/7xj/n444+bLEzJp0/i/fv3z/Dhw/Pcc8/lxRdfzHrrrZfrr78+PXr0yKWXXtrkSR3XXnvtPPTQQ/nDH/6Q22+/Pf/85z/zxBNPpGfPnllppZVy9NFHL/Th3HNfAVyU8wyss846GTlyZO6///48/PDDmTBhQp555pl88skn6dmzZ3beeefsscce+c53vtPkp4iccsopGThwYH7/+9/nkUceyb333psOHTqkd+/e2XrrrbPrrrs2KnJbb711/ud//idnnnlmHnzwwdx+++3p27dvTjrppBx99NHZaKONmszat2/f3H333TnttNPywAMP5K677kqfPn1ywgkn5Nhjj83gwYObvF27du3yu9/9Lvvtt1+uu+66/O///m/+9a9/pUuXLllhhRWy2267Zffdd68c0r+4+vTpk3/84x/5/e9/n1tvvTVPPPFEZs6cmeWXXz5bbbVVo49vXpL5HnjggdTV1WW99dab57K+ffsu9Mm7u3Tpkttvvz3Dhw/PX/7yl9xzzz1p3759Nt100xxxxBEL/L4DsHTSm/QmvWnB3nzzzTz55JONLn/xxRcr/7+wJ62HMtTU19d/8ZuEgdKcffbZOfPMM3PEEUfknHPOWSJrTJw4Mf37988GG2yQUaNGLZE1vmzrr79+xo8fn2eeeWaBH+cLACxd9KZFozcBRXJEE1SBsWPHpmfPnqmrq5vn8jvuuCMXXnhhampq8p3vfGeJrf/hhx/mhBNO+MKPeAUAKJveBFDdDJqgCtx000254IILssEGG2TllVfOrFmz8sorr+Tf//53kuSnP/1pNtxwwyW2/hprrJGf/exnS+z+AQCKojcBVDeDJqgCO++8c1599dU88cQTefnllzN9+vQsu+yy2W233XLYYYdVPpIXAKC105sAqptzNAEAAABQiDZlBwAAAABg6WDQBAAAAEAhDJoAAAAAKIRBEwAAAACFMGgCAAAAoBAGTQAAAAAUwqAJAAAAgEIYNAEAAABQCIMmAAAAAAph0AQAAABAIQyaAAAAACiEQRMAAAAAhTBoAgAAAKAQBk0AAAAAFMKgCQAAAIBCGDQBAAAAUAiDJgAAAAAKYdAEAAAAQCEMmgAAAAAohEETAAAAAIUwaAIAAACgEAZNAAAAABTCoAkAAACAQhg0AQAAAFAIgyYAAAAACmHQBAAAAEAhDJoAAAAAKIRBEwAAAACFMGgCAAAAoBAGTQAAAAAUwqAJAAAAgEIYNAEAAABQCIMmAAAAAAph0AQAAABAIQyaAAAAACiEQRMAAAAAhTBoAgAAAKAQBk0AAAAAFMKgCQAAAIBCGDQBAAAAUAiDJgAAAAAKYdAEAAAAQCEMmgAAAAAohEETAAAAAIUwaAIAAACgEAZNAAAAABTCoAkAAACAQhg0AQAAAFAIgyYAAAAACtGujEW7dOmSmpqaMpZOkgwbNqy0tefXv3//siMkSSZOnFh2hIr33nuv7AhJknHjxpUdoWKHHXYoO0KS5Kyzzio7QkWbNtUxJx8wYEDZESqq5WfniiuuKDtCxVVXXVV2hCTV9T0ZM2ZM2RGSJM8880xpa3fo0CF77LFHaes3l970H3pTY9Xyu19vakxvakxvaqyaOoLe1JjetGi9qZRBU01NTam/7KZMmVLa2vObPn162RGSJNOmTSs7QkW17J+PPvqo7AgV1fI4+eCDD8qOUFEthWnSpEllR6iolj98Zs+eXXaEismTJ5cdIUn1PF6T6tk/M2bMKDtCi6E3/Ue1PB/qTY3pTY3pTY3pTY1Vy/Nyojc1pVr2T0vrTdWzBwEAAABo0QyaAAAAACiEQRMAAAAAhTBoAgAAAKAQBk0AAAAAFMKgCQAAAIBCLPKg6amnnsq3vvWt9OnTJyuttFJ23nnn3HLLLUVmAwBo8XQmAKA1abcoNxo9enT22WefdOrUKUOGDEltbW1GjBiRQw89NBMmTMjRRx9ddE4AgBZHZwIAWptmD5o++eSTHHvssWnTpk1GjhyZDTbYIEnyk5/8JDvttFNOPfXU7LXXXunTp0/hYQEAWgqdCQBojZr91rnRo0fntddey7777lspTEnSvXv3HH/88Zk5c2ZuuOGGQkMCALQ0OhMA0Bo1e9D04IMPJkl23HHHRtt22mmnJMlDDz20mLEAAFo2nQkAaI2aPWgaO3ZskmSNNdZotK13796pra3Nq6++uvjJAABaMJ0JAGiNmj1o+uijj5Ik3bp1a3L7MsssU7kOAEBrpTMBAK1RswdNAAAAANCUZg+a5r4qt6BX4D7++OMFvnIHANBa6EwAQGvU7EHT3PMMzD3vwGe9++67mTx5clZfffXFTwYA0ILpTABAa9TsQdNWW22VJBk1alSjbffee+881wEAaK10JgCgNWr2oGm77bZL3759c/PNN+fZZ5+tXD5p0qRccMEF6dChQw444IBCQwIAtDQ6EwDQGrVr9g3atctvfvOb7LPPPhk4cGCGDBmS2trajBgxIuPHj8+pp56a1VZbbUlkBQBoMXQmAKA1avagKUm23Xbb3HnnnTnzzDNzyy23ZNasWVlnnXXy61//OkOGDCk6IwBAi6QzAQCtzSINmpJk4403zs0331xkFgCApY7OBAC0Js0+RxMAAAAANMWgCQAAAIBCGDQBAAAAUAiDJgAAAAAKYdAEAAAAQCEMmgAAAAAoRLsyFm1oaMicOXPKWDpJ0rVr19LWnl+nTp3KjpAk6dy5c9kRKqpl/3Tr1q3sCBXV8jjp2bNn2REq2rSpjjl59+7dy45QMWPGjLIjJEnatm1bdoSK2trasiMkSanPefOrlv3TsWPH0tbu0KFDaWsvCr3pP6rl+VBvakxvakxvakxvaqxanpcTvakp1bJ/Wlpvqqmvr29YAlkAAAAAaGWqY7QNAAAAQItn0AQAAABAIQyaAAAAACiEQRMAAAAAhTBoAgAAAKAQBk0AAAAAFMKgCQAAAIBCGDQBAAAAUAiDJgAAAAAKYdAEAAAAQCEMmgAAAAAohEETAAAAAIUwaAIAAACgEAZNAAAAABTCoAkAAACAQhg0AQAAAFAIgyYAAAAACmHQBAAAAEAhDJoAAAAAKIRBEwAAAACFaFfGol26dElNTU0ZSydJhg0bVtra8+vfv3/ZEZIkEydOLDtCxXvvvVd2hCTJuHHjyo5QscMOO5QdIUly1llnlR2hok2b6piTDxgwoOwIFdXys3PFFVeUHaHiqquuKjtCkur6nowZM6bsCEmSZ555prS1O3TokD322KO09ZvrvPPOy9SpU0tb/9prry1t7fntvffeZUdIkgwaNKjsCBXHHHNM2RGSJHvuuWfZESpeeumlsiMkSd56662yI1Q0NDSUHSFJcuihh5YdoWLw4MFlR0iSrL766mVHqLjgggvKjpAk6dGjR9kRKrp27Vp2hCTJU089VdraXbp0afYMpZRBU01NTal/JE6ZMqW0tec3ffr0siMkSaZNm1Z2hIpq2T8fffRR2REqquVx8sEHH5QdoaJaBk2TJk0qO0JFtQyMZ8+eXXaEismTJ5cdIUn1PF6T6tk/M2bMKDtCizF16tRSnxur6Xd/tfxMz5o1q+wIFR9++GHZEZJUT39Lque5uVqel5PqGTRV098c1fI9qaaOUC1/c1RTR2jfvn3ZEZKk1BecFkX1PKoBAAAAaNEMmgAAAAAohEETAAAAAIUwaAIAAACgEAZNAAAAABTCoAkAAACAQhg0AQAAAFCIZg+a/vznP+e4447L9ttvn+WXXz51dXW5/vrrl0Q2AIAWTW8CAFqbds29wWmnnZbx48enZ8+e6d27d8aPH78kcgEAtHh6EwDQ2jT7iKZLLrkkzz77bMaOHZvvfe97SyITAMBSQW8CAFqbZh/RtP322y+BGAAASx+9CQBobZwMHAAAAIBCGDQBAAAAUAiDJgAAAAAKYdAEAAAAQCEMmgAAAAAohEETAAAAAIUwaAIAAACgEAZNAAAAABSiXXNvcN111+WRRx5JkrzwwgtJkj/+8Y958MEHkyRbbLFFDj744AIjAgC0THoTANDaNHvQ9Mgjj+SGG26Y57JHH300jz76aOXfChMAgN4EALQ+zR40XX755bn88suXRBYAgKWK3gQAtDbO0QQAAABAIQyaAAAAACiEQRMAAAAAhTBoAgAAAKAQBk0AAAAAFKLZnzpXhIaGhsyZM6eMpZMkXbt2LW3t+XXq1KnsCEmSzp07lx2holr2T7du3cqOUFEtj5OePXuWHaGiTZvqmJN379697AgVM2bMKDtCkqRt27ZlR6iora0tO0KSlPqcN79q2T8dO3Ysbe0OHTqUtvai6NKlS6nrV9Pv/mr5mW7fvn3ZESqWXXbZsiMkqZ7+llTPc/OUKVPKjlDR0NBQdoQk1fU3R01NTdkRklRXR6iWvznK7Ajzq5bOUGYXWJS1a+rr66vjtw4AAAAALVp1HBIAAAAAQItn0AQAAABAIQyaAAAAACiEQRMAAAAAhTBoAgAAAKAQBk0AAAAAFMKgCQAAAIBCGDQBAAAAUAiDJgAAAAAKYdAEAAAAQCEMmgAAAAAohEETAAAAAIUwaAIAAACgEAZNAAAAABTCoAkAAACAQhg0AQAAAFAIgyYAAAAACmHQBAAAAEAhDJoAAAAAKIRBEwAAAACFaFfGol26dElNTU0ZSydJhg0bVtra8+vfv3/ZEZIkEydOLDtCxXvvvVd2hCTJuHHjyo5QscMOO5QdIUly1llnlR2hok2b6piTDxgwoOwIFdXys3PFFVeUHaHiqquuKjtCkur6nowZM6bsCEmSZ555prS1O3TokD322KO09ZtLb/oPvamxavndrzc1pjc1pjc1Vk0dQW9qTG9atN5UyqCppqam1F92U6ZMKW3t+U2fPr3sCEmSadOmlR2holr2z0cffVR2hIpqeZx88MEHZUeoqJbCNGnSpLIjVFTLHz6zZ88uO0LF5MmTy46QpHoer0n17J8ZM2aUHaHF0Jv+o1qeD/WmxvSmxvSmxvSmxqrleTnRm5pSLfunpfWm6tmDAAAAALRoBk0AAAAAFMKgCQAAAIBCGDQBAAAAUAiDJgAAAAAKYdAEAAAAQCEMmgAAAAAoRLMHTW+99VYuu+yy7L333llvvfXSq1evrLXWWjnooIPy5JNPLomMAAAtjs4EALRG7Zp7gyuvvDIXXXRR+vXrlx122CHLLbdcxo4dm5EjR2bkyJG56qqrMmTIkCWRFQCgxdCZAIDWqNmDpgEDBuT222/P1ltvPc/lDz/8cPbaa68cf/zxGThwYDp27FhYSACAlkZnAgBao2a/dW7PPfdsVJiSZMstt8w222yT+vr6vPDCC4WEAwBoqXQmAKA1KvRk4O3bt0+StG3btsi7BQBYquhMAMDSqrBB0/jx43PfffdlhRVWyLrrrlvU3QIALFV0JgBgaVbIoGnWrFn5wQ9+kBkzZuRXv/qVV+cAAJqgMwEAS7vFHjTNmTMnRx55ZB5++OEccsghOeCAA4rIBQCwVNGZAIDWYLEGTXPmzMlRRx2Vv/zlL9lvv/1y4YUXFpULAGCpoTMBAK1Fu0W94dxX5W688cbsu+++ufzyy9OmTaHnFgcAaPF0JgCgNVmklvPZwjRkyJD89re/dY4BAID56EwAQGvT7EHT3EO/b7zxxgwePDhXXnmlwgQAMB+dCQBojZr91rmzzz47N9xwQ2pra7Pmmmvm3HPPbXSdgQMHZoMNNigkIABAS6QzAQCtUbMHTW+88UaSZPLkyTnvvPOavE6fPn2UJgCgVdOZAIDWqNmDpssvvzyXX375ksgCALDU0JkAgNbIR54AAAAAUAiDJgAAAAAKYdAEAAAAQCEMmgAAAAAohEETAAAAAIVo9qfOFaGhoSFz5swpY+kkSdeuXUtbe36dOnUqO0KSpHPnzmVHqKiW/dOtW7eyI1RUy+OkZ8+eZUeoaNOmOubk3bt3LztCxYwZM8qOkCRp27Zt2REqamtry46QJKU+582vWvZPx44dS1u7Q4cOpa29KPSm/6iW50O9qTG9qTG9qTG9qbFqeV5O9KamVMv+aWm9qaa+vr5hCWQBAAAAoJWpjtE2AAAAAC2eQRMAAAAAhTBoAgAAAKAQBk0AAAAAFMKgCQAAAIBCGDQBAAAAUAiDJgAAAAAKYdAEAAAAQCEMmgAAAAAohEETAAAAAIUwaAIAAACgEAZNAAAAABTCoAkAAACAQhg0AQAAAFAIgyYAAAAACmHQBAAAAEAhDJoAAAAAKIRBEwAAAACFMGgCAAAAoBAGTQAAAAAUol0Ziw4aNCj19fVlLJ0keffdd0tbe34HHXRQ2RGSJA899FDZESqWX375siMkSbbddtuyI1ScddZZZUdIkiy33HJlR6jo0aNH2RGSJIceemjZESo22mijsiMkScaPH192hIrBgweXHSFJ8otf/KLsCBXbbLNN2RGSJFdddVVpa9fW1ubiiy8ubf3m6tKlS2pqakpbf9iwYaWtPb/+/fuXHSFJMnHixLIjVLz33ntlR0iSjBs3ruwIFTvssEPZEZJUT39LkjZtquP4ggEDBpQdoaJafnauuOKKsiNUlPnc/FnV9D0ZM2ZM2RGSJM8880xpa3fo0CF77LFHs25TyqCpvr6+1CfoDz74oLS15zdlypSyIyRJqYO/+XXq1KnsCEmSadOmlR2holoes23bti07QkWZf3R91owZM8qOUDF79uyyIyRJZs2aVXaEimop1lOnTi07QkW17J+PP/647AgtRk1NTamP5WrpKkkyffr0siMkqa6OUC3756OPPio7QkW1PE6qpb8l1fN8OGnSpLIjVFTLwLha+luSTJ48uewISarn8ZpUz/6ppr85Fkb17EEAAAAAWjSDJgAAAAAKYdAEAAAAQCEMmgAAAAAohEETAAAAAIUwaAIAAACgEAZNAAAAABSiXXNvMH369JxyyikZM2ZMXnvttUycODHdu3dPv379ctBBB2X//fdP+/btl0RWAIAWQ2cCAFqjZh/RNGXKlFx99dWpqanJLrvskqOOOiqDBg3KW2+9lR/+8IfZf//9M2fOnCWRFQCgxdCZAIDWqNlHNPXo0SNvvPFGOnToMM/ln3zySQYPHpxRo0blnnvuya677lpYSACAlkZnAgBao2Yf0dSmTZtGhSlJ2rVrl0GDBiVJXn311cVPBgDQgulMAEBrVNjJwOfMmZN77703SbLOOusUdbcAAEsVnQkAWJo1+61zc82cOTPnn39+GhoaMnHixNx///15+eWXc+CBB2a77bYrMiMAQIulMwEArcliDZrOPvvsyr9rampy9NFH55e//GUhwQAAlgY6EwDQmizyW+dqa2tTX1+fDz/8MP/85z9z3nnn5brrrsugQYPy0UcfFZkRAKDF0pkAgNZksc/R1KZNm6y88sr5/ve/n4svvjiPPvpozj///CKyAQAsNXQmAKA1KOxk4Emyww47JEkefPDBIu8WAGCpojMBAEurQgdN77zzTpKkffv2Rd4tAMBSRWcCAJZWzR40vfjii5k6dWqjy6dOnZqf//znSZJvfOMbi58MAKAF05kAgNao2Z86d8stt+Syyy7L5ptvnj59+mSZZZbJW2+9lf/5n//Jhx9+mC222CJHHnnkksgKANBi6EwAQGvU7EHTbrvtlnfeeSePP/54Hn/88UyZMiXdunXLuuuum3322Sff/e53065ds+8WAGCpojMBAK1Rs9vNRhttlI022mhJZAEAWGroTABAa1ToycABAAAAaL0MmgAAAAAohEETAAAAAIUwaAIAAACgEAZNAAAAABSilM/UraurK2PZipkzZ5a6/md17dq17AhJyt8nn9WtW7eyIyRJOnfuXHaEip49e5YdIUnSo0ePsiNUVMtjtmPHjmVHqGjbtm3ZEZIk7du3LztCxZw5c8qOkCTp0qVL2REqqmX/LLPMMqWtXVtbW9rai6KhoaHUx3K1dJUk6dSpU9kRklRXR6iW/VMt/S2pnsdJtfS3JGnTpjqOL+jevXvZESpmzJhRdoQk1dPfkup5fqyW/pZUz/4p82+ODh06NPs2NfX19Q1LIAsAAAAArUx1jLYBAAAAaPEMmgAAAAAohEETAAAAAIUwaAIAAACgEAZNAAAAABTCoAkAAACAQhg0AQAAAFAIgyYAAAAACmHQBAAAAEAhDJoAAAAAKIRBEwAAAACFMGgCAAAAoBAGTQAAAAAUwqAJAAAAgEIYNAEAAABQCIMmAAAAAAph0AQAAABAIQyaAAAAACiEQRMAAAAAhTBoAgAAAKAQ7cpYtEuXLqmpqSlj6STJsGHDSlt7fv379y87QpJk4sSJZUeoeO+998qOkCQZN25c2REqdthhh7IjJEnOOuussiNUtGlTHXPyAQMGlB2holp+dq644oqyI1RcddVVZUdIUl3fkzFjxpQdIUnyzDPPlLZ2hw4dsscee5S2fnOdd955mTp1amnrX3vttaWtPb+999677AhJkkGDBpUdoeKYY44pO0KSZM899yw7QsVLL71UdoQkyVtvvVV2hIqGhoayIyRJDj300LIjVAwePLjsCEmS1VdfvewIFRdccEHZEZIkPXr0KDtCRdeuXcuOkCR56qmnSlu7S5cuzZ6hlDJoqqmpKfWPxClTppS29vymT59edoQkybRp08qOUFEt++ejjz4qO0JFtTxOPvjgg7IjVFTLoGnSpEllR6ioloHx7Nmzy45QMXny5LIjJKmex2tSPftnxowZZUdoMaZOnVrqc2M1/e6vlp/pWbNmlR2h4sMPPyw7QpLq6W9J9Tw3V8vzclI9g6Zq+pujWr4n1dQRquVvjmrqCO3bty87QpKU+oLToqieRzUAAAAALZpBEwAAAACFMGgCAAAAoBAGTQAAAAAUwqAJAAAAgEIYNAEAAABQCIMmAAAAAApRyKDpoosuSl1dXerq6vLEE08UcZcAAEslvQkAWJot9qDphRdeyJlnnpmuXbsWkQcAYKmlNwEAS7vFGjTNmjUrQ4cOzfrrr5+BAwcWlQkAYKmjNwEArcFiDZrOO++8vPjii7n00kvTtm3bojIBACx19CYAoDVY5EHT008/nfPPPz8nnnhivvrVrxaZCQBgqaI3AQCtxSINmmbMmFE59PvYY48tOhMAwFJDbwIAWpN2i3KjM844I2PHjs19993n0G8AgM+hNwEArUmzj2h6/PHHc8kll+RHP/pR1llnnSWRCQBgqaA3AQCtTbMGTZ988kmGDh2addddN8OGDVtSmQAAWjy9CQBojZr11rnJkydn7NixSZJevXo1eZ1vfOMbSZI//elPGTRo0GLGAwBomfQmAKA1atagqWPHjjnooIOa3Pbwww9n7Nix2X333bPccsulT58+hQQEAGiJ9CYAoDVq1qCpc+fOueSSS5rcNnTo0IwdOzbHH398Nt1000LCAQC0VHoTANAaNftk4AAAAADQFIMmAAAAAApR2KDp8ssvT319vcO/AQC+gN4EACytHNEEAAAAQCEMmgAAAAAohEETAAAAAIUwaAIAAACgEAZNAAAAABSiXRmLNjQ0ZM6cOWUsnSTp2rVraWvPr1OnTmVHSJJ07ty57AgV1bJ/unXrVnaEimp5nPTs2bPsCBVt2lTHnLx79+5lR6iYMWNG2RGSJG3bti07QkVtbW3ZEZKk1Oe8+VXL/unYsWNpa3fo0KG0tRdFly5dSl2/mn73V8vPdPv27cuOULHsssuWHSFJ9fS3pHqem6dMmVJ2hIqGhoayIySprr85ampqyo6QpLo6QrX8zVFmR5hftXSGMrvAoqxdU19fXx2/dQAAAABo0arjkAAAAAAAWjyDJgAAAAAKYdAEAAAAQCEMmgAAAAAohEETAAAAAIUwaAIAAACgEAZNAAAAABTCoAkAAACAQhg0AQAAAFAIgyYAAAAACmHQBAAAAEAhDJoAAAAAKIRBEwAAAACFMGgCAAAAoBAGTQAAAAAUwqAJAAAAgEIYNAEAAABQCIMmAAAAAAph0AQAAABAIQyaAAAAAChEuzIW7dKlS2pqaspYOkkybNiw0taeX//+/cuOkCSZOHFi2REq3nvvvbIjJEnGjRtXdoSKHXbYoewISZKzzjqr7AgVbdpUx5x8wIABZUeoqJafnSuuuKLsCBVXXXVV2RGSVNf3ZMyYMWVHSJI888wzpa3doUOH7LHHHqWt31x603/oTY1Vy+9+vakxvakxvamxauoIelNjetOi9aZSBk01NTWl/rKbMmVKaWvPb/r06WVHSJJMmzat7AgV1bJ/Pvroo7IjVFTL4+SDDz4oO0JFtRSmSZMmlR2holr+8Jk9e3bZESomT55cdoQk1fN4Tapn/8yYMaPsCC2G3vQf1fJ8qDc1pjc1pjc1pjc1Vi3Py4ne1JRq2T8trTdVzx4EAAAAoEUzaAIAAACgEAZNAAAAABTCoAkAAACAQhg0AQAAAFAIgyYAAAAACmHQBAAAAEAh2i3KjdZff/2MHz++yW1bbbVVRo4cuVihAACWBjoTANDaLNKgKUm6deuWoUOHNrq8T58+ixUIAGBpojMBAK3JIg+aunfvnp/97GdFZgEAWOroTABAa+IcTQAAAAAUYpGPaJo5c2auv/76vPPOO1lmmWUyYMCAbLLJJkVmAwBo8XQmAKA1WeRB07vvvpujjjpqnssGDBiQ3//+9+nXr99iBwMAWBroTABAa7JIb5078MADc+utt+bf//533nrrrYwePTr7779/nnrqqey55575+OOPi84JANDi6EwAQGuzSEc0/fSnP53n3xtssEF++9vfJkn+/Oc/59prr80Pf/jDxU8HANCC6UwAQGtT6MnADz300CTJY489VuTdAgAsVXQmAGBpVeigqWfPnkmSqVOnFnm3AABLFZ0JAFhaFTpoevLJJ5Mkffr0KfJuAQCWKjoTALC0avag6eWXX27y1beXX345v/rVr5Ik++6772IHAwBoyXQmAKA1avbJwP/617/msssuy5ZbbplVV101Xbp0ySuvvJJ77rkns2bNyvHHH5+tttpqSWQFAGgxdCYAoDVq9qBpm222ycsvv5xnn302jzzySKZOnZqePXvmG9/4Rg477LDsuOOOSyInAECLojMBAK1RswdNW2+9dbbeeuslkQUAYKmhMwEArVGhJwMHAAAAoPUyaAIAAACgEAZNAAAAABTCoAkAAACAQhg0AQAAAFCIZn/qXBEaGhoyZ86cMpZOknTt2rW0tefXqVOnsiMkSTp37lx2hIpq2T/dunUrO0JFtTxOevbsWXaEijZtqmNO3r1797IjVMyYMaPsCEmStm3blh2hora2tuwISVLqc978qmX/dOzYsbS1O3ToUNrai0Jv+o9qeT7UmxrTmxrTmxrTmxqrluflRG9qSrXsn5bWm2rq6+sblkAWAAAAAFqZ6hhtAwAAANDiGTQBAAAAUAiDJgAAAAAKYdAEAAAAQCEMmgAAAAAohEETAAAAAIUwaAIAAACgEAZNAAAAABTCoAkAAACAQhg0AQAAAFAIgyYAAAAACmHQBAAAAEAhDJoAAAAAKIRBEwAAAACFMGgCAAAAoBAGTQAAAAAUwqAJAAAAgEIYNAEAAABQCIMmAAAAAAph0AQAAABAIdqVsWiXLl1SU1NTxtJJkmHDhpW29vz69+9fdoQkycSJE8uOUPHee++VHSFJMm7cuLIjVOywww5lR0iSnHXWWWVHqGjTpjrm5AMGDCg7QkW1/OxcccUVZUeouOqqq8qOkKS6vidjxowpO0KS5Jlnnilt7Q4dOmSPPfYobf3m0pv+Q29qrFp+9+tNjelNjelNjVVTR9CbGtObFq03lTJoqqmpKfWX3ZQpU0pbe37Tp08vO0KSZNq0aWVHqKiW/fPRRx+VHaGiWh4nH3zwQdkRKqqlME2aNKnsCBXV8ofP7Nmzy45QMXny5LIjJKmex2tSPftnxowZZUdoMfSm/6iW50O9qTG9qTG9qTG9qbFqeV5O9KamVMv+aWm9qXr2IAAAAAAtmkETAAAAAIUwaAIAAACgEAZNAAAAABTCoAkAAACAQhg0AQAAAFAIgyYAAAAACrFYg6bbbrstgwcPTr9+/dK7d+9ssMEG+f73v58JEyYUlQ8AoMXTmQCA1qLdotyooaEhw4YNyzXXXJN+/fpln332SW1tbd5+++089NBDGT9+fFZZZZWiswIAtCg6EwDQ2izSoOmKK67INddck8MOOyxnn3122rZtO8/2Tz75pJBwAAAtmc4EALQ2zX7r3LRp03L22Wenb9++OeussxoVpiRp126R5lcAAEsNnQkAaI2a3W5GjRqV+vr6HHjggZk9e3buuOOOjB07Nt27d8/222+f1VdffUnkBABoUXQmAKA1avag6emnn06StG3bNltttVVeeeWVyrY2bdrkyCOPzGmnnVZYQACAlkhnAgBao2a/de79999PkgwfPjzdunXLqFGjMmHChNxxxx1Zc801c+mll+b3v/994UEBAFoSnQkAaI2aPWiaM2dOkqRDhw65/vrrM2DAgNTW1mbLLbfMNddckzZt2uTSSy8tPCgAQEuiMwEArVGzB03dunVLkmy44YZZccUV59m2zjrrpG/fvnnttddSX19fSEAAgJZIZwIAWqNmD5r69++fJOnevXuT2+dePn369MWIBQDQsulMAEBr1OxB0zbbbJMkefnllxttmzVrVl599dV07do1yy233OKnAwBooXQmAKA1avagqV+/ftlxxx3z6quv5rrrrptn24UXXphJkyZl4MCBadeu2R9oBwCw1NCZAIDWaJGazfnnn59ddtklxxxzTEaOHJn+/fvn2WefzejRo7Pqqqvm1FNPLTonAECLozMBAK1Ns49oSj59he4f//hHvvOd7+Tpp5/Ob3/727z66qs5/PDDM2rUqPTu3bvonAAALY7OBAC0Not8rPYqq6ySyy67rMgsAABLHZ0JAGhNFumIJgAAAACYn0ETAAAAAIUwaAIAAACgEAZNAAAAABTCoAkAAACAQizyp84tjoaGhsyZM6eMpZMkXbt2LW3t+XXq1KnsCEmSzp07lx2holr2T7du3cqOUFEtj5OePXuWHaGiTZvqmJN379697AgVM2bMKDtCkqRt27ZlR6iora0tO0KSlPqcN79q2T8dO3Ysbe0OHTqUtvai0Jv+o1qeD/WmxvSmxvSmxvSmxqrleTnRm5pSLfunpfWmmvr6+oYlkAUAAACAVqY6RtsAAAAAtHgGTQAAAAAUwqAJAAAAgEIYNAEAAABQCIMmAAAAAAph0AQAAABAIQyaAAAAACiEQRMAAAAAhTBoAgAAAKAQBk0AAAAAFMKgCQAAAIBCGDQBAAAAUAiDJgAAAAAKYdAEAAAAQCEMmgAAAAAohEETAAAAAIUwaAIAAACgEAZNAAAAABTCoAkAAACAQhg0AQAAAFCIdmUsOmjQoNTX15exdJLk3XffLW3t+R100EFlR0iSPPTQQ2VHqFh++eXLjpAk2XbbbcuOUHHWWWeVHSFJstxyy5UdoaJHjx5lR0iSHHrooWVHqNhoo43KjpAkGT9+fNkRKgYPHlx2hCTJL37xi7IjVGyzzTZlR0iSXHXVVaWtXVtbm4svvri09ZvrvPPOy9SpU0tb/9prry1t7fntvffeZUdI8mmXrRbHHHNM2RGSJHvuuWfZESpeeumlsiMkSd56662yI1Q0NDSUHSFJdfWmaukIq6++etkRKi644IKyIySpnp6fJF27di07QpLkqaeeKm3tLl26ZNiwYc26TSmDpvr6+kycOLGMpZMkH3zwQWlrz2/KlCllR0iSUgd/8+vUqVPZEZIk06ZNKztCRbU8Ztu2bVt2hIqampqyIyRJZsyYUXaEitmzZ5cdIUkya9assiNUtGlTHQfuljkkmF+17J+PP/647AgtxtSpU0vtC9XyHJQkkydPLjtCkur5OUqSDz/8sOwISaqn0ybJpEmTyo6QJKX+vTO/ahk0VVO/rpbvSbV0lSSZPn162RGSVFe/bt++fdkRklRXl1wY1fOoBgAAAKBFM2gCAAAAoBAGTQAAAAAUwqAJAAAAgEIYNAEAAABQCIMmAAAAAAph0AQAAABAIZo9aLr++utTV1f3uf/tueeeSyIrAECLoTMBAK1Ru+beYP3118+JJ57Y5LYRI0bkX//6V3baaafFDgYA0JLpTABAa9TsQdMGG2yQDTbYoNHlM2fOzO9+97u0a9cu3/72twsJBwDQUulMAEBrVNg5mkaOHJkPP/wwu+66a5Zffvmi7hYAYKmiMwEAS7PCBk3XXXddkuTggw8u6i4BAJY6OhMAsDQrZND0xhtv5P7778/KK6+cnXfeuYi7BABY6uhMAMDSrpBB0/XXX585c+bk29/+dtq2bVvEXQIALHV0JgBgabfYg6Y5c+bk+uuvT01NTb773e8WkQkAYKmjMwEArcFiD5ruu+++TJgwIdtuu2369u1bQCQAgKWPzgQAtAaLPWhyQksAgC+mMwEArcFiDZo+/PDD3HHHHenRo0cGDRpUVCYAgKWKzgQAtBaLNWi68cYbM3PmzOy3337p2LFjUZkAAJYqOhMA0Fos1qDpT3/6UxKHgAMAfB6dCQBoLRZ50PS///u/eeGFF7Lxxhtn3XXXLTITAMBSQ2cCAFqTdot6w4033jj19fUFRgEAWProTABAa7LYnzoHAAAAAIlBEwAAAAAFMWgCAAAAoBAGTQAAAAAUwqAJAAAAgEIYNAEAAABQiHZlLFpXV1fGshUzZ84sdf3P6tq1a9kRkpS/Tz6rW7duZUdIknTu3LnsCBU9e/YsO0KSpEePHmVHqKiWx2zHjh3LjlDRtm3bsiMkSdq3b192hIo5c+aUHSFJ0qVLl7IjVFTL/llmmWVKW7u2tra0tRdF2Y+fankOSqpn31XLz1GSLLvssmVHSFI9nTZJunfvXnaEJMmUKVPKjlDR0NBQdoQk1dWva2pqyo6QpHq6SpJ06tSp7AhJqqtfd+jQoewIScrtAouydk19fX11/NYBAAAAoEXz1jkAAAAACmHQBAAAAEAhDJoAAAAAKIRBEwAAAACFMGgCAAAAoBAGTQAAAAAUwqAJAAAAgEIYNAEAAABQCIMmAAAAAAph0AQAAABAIQyaAAAAACiEQRMAAAAAhTBoAgAAAKAQBk0AAAAAFMKgCQAAAIBCGDQBAAAAUAiDJgAAAAAKYdAEAAAAQCEMmgAAAAAoRLsyFu3SpUtqamrKWDpJMmzYsNLWnl///v3LjpAkmThxYtkRKt57772yIyRJxo0bV3aEih122KHsCEmSs846q+wIFW3aVMecfMCAAWVHqKiWn50rrrii7AgVV111VdkRklTX92TMmDFlR0iSPPPMM6Wt3aFDh+yxxx6lrd9cetN/6E2NVcvvfr2pMb2pMb2psWrqCHpTY3rTovWmUgZNNTU1pf6ymzJlSmlrz2/69OllR0iSTJs2rewIFdWyfz766KOyI1RUy+Pkgw8+KDtCRbUUpkmTJpUdoaJa/vCZPXt22REqJk+eXHaEJNXzeE2qZ//MmDGj7Agtht70H9XyfKg3NaY3NaY3NaY3NVYtz8uJ3tSUatk/La03Vc8eBAAAAKBFM2gCAAAAoBAGTQAAAAAUwqAJAAAAgEIYNAEAAABQCIMmAAAAAAph0AQAAABAIRZp0NTQ0JARI0Zk0KBB+cpXvpIVV1wxm2yySY477riMGzeu4IgAAC2TzgQAtDaLNGg6+eSTc/DBB+eVV17JwIEDc8QRR2S11VbLtddem2222SYvvPBC0TkBAFocnQkAaG3aNfcG7777bi6//PKsuuqqefDBB9O9e/fKtuHDh+fnP/95hg8fnuHDhxcaFACgJdGZAIDWqNlHNL3xxhuZM2dONt9883kKU5LstttuSZL333+/mHQAAC2UzgQAtEbNHjStscYa6dChQx599NF89NFH82y78847kyTbbbddMekAAFoonQkAaI2a/da5ZZddNr/85S9z8skn5+tf/3q++c1vZplllsnzzz+f0aNH57DDDssRRxyxJLICALQYOhMA0Bo1e9CUJEcddVRWWmmlHHPMMbn66qsrl2+xxRbZd999067dIt0tAMBSRWcCAFqbRfrUubPPPjtHHHFEjj/++Pzzn//MhAkT8ve//z3Tp0/PoEGDcscddxSdEwCgxdGZAIDWptmDpvvuuy9nnnlmDj/88AwbNiwrr7xyamtrs8UWW+TGG29M+/btc/LJJy+JrAAALYbOBAC0Rs0eNN1zzz1Jkm222abRtt69e6d///559dVXM3ny5MVPBwDQQulMAEBr1OxB08yZM5Ms+ON4P/jgg7Rp0ybt27dfvGQAAC2YzgQAtEbNHjRtvvnmSZLLLrsskyZNmmfb1VdfnTfffDNf//rX07Fjx2ISAgC0QDoTANAaNfujTgYPHpzf//73efjhh7PJJptk9913T/fu3fPMM89k9OjR6dy5c04//fQlkRUAoMXQmQCA1qjZg6a2bdvmlltuyWWXXZZbbrklN998c2bOnJnll18+++23X0444YR85StfWRJZAQBaDJ0JAGiNmj1oSpKOHTtm2LBhGTZsWNF5AACWGjoTANDaNPscTQAAAADQFIMmAAAAAAph0AQAAABAIQyaAAAAACiEQRMAAAAAhTBoAgAAAKAQ7cpYtKGhIXPmzClj6SRJ165dS1t7fp06dSo7QpKkc+fOZUeoqJb9061bt7IjVFTL46Rnz55lR6ho06Y65uTdu3cvO0LFjBkzyo6QJGnbtm3ZESpqa2vLjpAkpT7nza9a9k/Hjh1LW7tDhw6lrb0o9Kb/qJbnQ72pMb2pMb2pMb2psWp5Xk70pqZUy/5pab2ppr6+vmEJZAEAAACglamO0TYAAAAALZ5BEwAAAACFMGgCAAAAoBAGTQAAAAAUwqAJAAAAgEIYNAEAAABQCIMmAAAAAArRYgZNTz31VL71rW+lT58+WWmllbLzzjvnlltuKTtWKd56661cdtll2XvvvbPeeuulV69eWWuttXLQQQflySefLDte1bjoootSV1eXurq6PPHEE2XHKc1tt92WwYMHp1+/fundu3c22GCDfP/738+ECRPKjvala2hoyIgRIzJo0KB85StfyYorrphNNtkkxx13XMaNG1d2vCXqz3/+c4477rhsv/32WX755VNXV5frr79+gdf/6KOPctJJJ2W99dbL8ssvn/XXXz+/+MUvMnny5C8x9ZK1sN+TWbNm5dZbb83/+3//L1//+tez8sorZ5VVVslOO+2U3//+95k9e3YJ6ZeM5j5OPmvcuHFZeeWVU1dXl2HDhi3hpHwenek/dKaFpzd9Sm/6j9bam3SmxnSmxnSmz9eu7AALY/To0dlnn33SqVOnDBkyJLW1tRkxYkQOPfTQTJgwIUcffXTZEb9UV155ZS666KL069cvO+ywQ5ZbbrmMHTs2I0eOzMiRI3PVVVdlyJAhZccs1QsvvJAzzzwzXbt2zZQpU8qOU4qGhoYMGzYs11xzTfr165d99tkntbW1efvtt/PQQw9l/PjxWWWVVcqO+aU6+eSTM3z48KywwgoZOHBglllmmTz//PO59tpr89e//jV33XVX1llnnbJjLhGnnXZaxo8fn549e6Z3794ZP378Aq87ZcqUDBw4MM8991x23HHH7Lvvvnn22WdzySWX5KGHHsodd9yRTp06fYnpl4yF/Z689tprOeSQQ1JbW5ttt902u+++ez766KPceeedOeGEE3L33XfnxhtvTE1NzZf8FRSvOY+Tz5ozZ06GDh26hNOxMHSmeelMC0dv0pua0lp7k87UmM7UmM70+ap+0PTJJ5/k2GOPTZs2bTJy5MhssMEGSZKf/OQn2WmnnXLqqadmr732Sp8+fUpO+uUZMGBAbr/99my99dbzXP7www9nr732yvHHH5+BAwemY8eOJSUs16xZszJ06NCsv/76WX311XPTTTeVHakUV1xxRa655pocdthhOfvss9O2bdt5tn/yySclJSvHu+++m8svvzyrrrpqHnzwwXTv3r2ybfjw4fn5z3+e4cOHZ/jw4SWmXHIuueSSrL766unTp08uvPDC/PrXv17gdS+++OI899xzOe644/KrX/2qcvmvfvWrXHTRRbnsssty/PHHfwmpl6yF/Z7U1tbmvPPOy7e//e107dq1cvlpp52WQYMG5a677sqtt96awYMHf0nJl5zmPE4+a/jw4XniiSdyyimn5KSTTlrCKVkQnakxnemL6U2f0pvm1Zp7k87UmM7UmM70+ar+rXOjR4/Oa6+9ln333bdSmJKke/fuOf744zNz5szccMMNJSb88u25556NClOSbLnlltlmm21SX1+fF154oYRk1eG8887Liy++mEsvvbRRSWgtpk2blrPPPjt9+/bNWWed1eT3oV27qp8zF+qNN97InDlzsvnmm89TlpJkt912S5K8//77ZUT7Umy//fYL9cdlQ0ND/vjHP6a2tjY//vGP59n24x//OLW1tbnuuuuWVMwv1cJ+T1ZaaaUcdthh8xSmJOnatWuOOuqoJMlDDz20RDJ+2Rb2e/JZL7/8ck4//fQMGzYs66+//hJKxsLQmRrTmb6Y3qQ3NaU19yadqTGdqTGd6fNV/aDpwQcfTJLsuOOOjbbttNNOSZaeB2sR2rdvnySttig8/fTTOf/883PiiSfmq1/9atlxSjNq1KjU19dn4MCBmT17dkaMGJELL7wwV199dV599dWy45VijTXWSIcOHfLoo4/mo48+mmfbnXfemSTZbrvtyohWVcaOHZu33347m222WZMlYbPNNsu4ceNa5bkqmtLaf+fOnj07Q4cOzeqrr96oZPPl05map7X//CZ601x6U2N60xfTmZqntf/ObW2dqepH82PHjk3y6S+7+fXu3Tu1tbWt9glgfuPHj899992XFVZYIeuuu27Zcb50M2bMqBz6feyxx5Ydp1RPP/10kk9/kW+11VZ55ZVXKtvatGmTI488MqeddlpJ6cqx7LLL5pe//GVOPvnkfP3rX883v/nNyrkGRo8encMOOyxHHHFE2TFLN/d37uqrr97k9tVXXz333ntvxo4d2+rOVdGUP/3pT0ma/sO+NbjgggvyzDPP5H/+53/SoUOHsuO0ejrTwmvtnSnRmz5Lb2pMb/piOlPz6EytqzNV/aBp7gS9W7duTW5fZpllGk3ZW6NZs2blBz/4QWbMmJFf/epXrXJSfMYZZ2Ts2LG57777WuXX/1lzD2UePnx4vva1r2XUqFFZa6218uyzz+a4447LpZdemn79+uX73/9+yUm/XEcddVRWWmmlHHPMMbn66qsrl2+xxRbZd999W91h8U2Z+/t0/sPk55r7u9jv3eSaa67JPffck2233Ta77LJL2XG+dM8991zOOeecHHPMMdlwww3LjkN0poWlM31Kb/oPvalpetPn05kWns7U+jpT1b91ji82Z86cHHnkkXn44YdzyCGH5IADDig70pfu8ccfzyWXXJIf/ehHS+WnXzTXnDlzkiQdOnTI9ddfnwEDBqS2tjZbbrllrrnmmrRp0yaXXnppySm/fGeffXaOOOKIHH/88fnnP/+ZCRMm5O9//3umT5+eQYMG5Y477ig7Ii3EnXfemR//+MdZddVVc+WVV5Yd50s3c+bMyuHfJ554YtlxYKHpTJ/Sm+alNzVNb6IIOlPr7ExVP2j6oknwxx9/vMBX7lqDOXPm5Kijjspf/vKX7LfffrnwwgvLjvSl++STTzJ06NCsu+66GTZsWNlxqsLcn4kNN9wwK6644jzb1llnnfTt2zevvfZa6uvrS0hXjvvuuy9nnnlmDj/88AwbNiwrr7xyamtrs8UWW+TGG29M+/btc/LJJ5cds3RzHzuTJk1qcvsXHTHRGtx999055JBDsvzyy+e2227LCiusUHakL90FF1yQF154IcOHD2/Vn9ZVbXSmz6czfUpvakxvakxv+mI60xfTmVpvZ6r6QdPc8wzMfQ/sZ7377ruZPHnyAt8Xu7Sb+6rcDTfckH333TeXX3552rSp+l1auMmTJ2fs2LF57rnn0qtXr9TV1VX+m/vpOt/4xjdSV1eX22+/veS0X47+/fsnWfChvHMvnz59+peWqWz33HNPkmSbbbZptK13797p379/Xn311UyePPnLjlZV5v7OXdB5XOZe3tQ5YFqDu+66KwcddFB69uyZ2267LX379i07UimeffbZzJkzJzvvvPM8v3P32GOPJMkf/vCH1NXV5Tvf+U7JSVsXnWnBdKb/0Jsa05sa05u+mM70+XSmT7XWzlT1b6zdaqutcsEFF2TUqFHZZ5995tl27733Vq7T2swtTDfeeGOGDBmS3/72t632/fUdO3bMQQcd1OS2hx9+OGPHjs3uu++e5ZZbrtkfQdlSzS0FL7/8cqNts2bNyquvvpquXbtmueWW+7KjlWbmzJlJFvxRvB988EHatGlT+USM1mqNNdbIiiuumMceeyxTpkyZ51NUpkyZksceeyyrrbZaqzyp5V133ZWDDz44PXr0yG233dZq/2BPkh122CE9e/ZsdPm7776bu+++O2uttVY222yzbLDBBiWka710pqbpTPPSmxrTmxrTm76YzrRgOtN/tNbOVPWDpu222y59+/bNzTffnB/84AeVHTBp0qRccMEF6dChQ6t7f/3cQ79vvPHGDB48OFdeeWWrLkydO3fOJZdc0uS2oUOHZuzYsTn++OOz6aabfsnJytOvX7/suOOOGTVqVK677rocfPDBlW0XXnhhJk2alP32269VncRx8803z+9+97tcdtll2XPPPed51fLqq6/Om2++mc0337xVHdLalJqamhx00EE555xzcu655+ZXv/pVZdu5556byZMn5/jjjy8vYEnuueeeHHzwwamrq8ttt93Wal+dnOvwww9v8vIHHnggd999d7baaqtW+7akMulMjelMjelNjelNjelNX0xnaprONK/W2pmq/rdlu3bt8pvf/Cb77LNPBg4cmCFDhqS2tjYjRozI+PHjc+qpp2a11VYrO+aX6uyzz84NN9yQ2trarLnmmjn33HMbXWfgwIFL3VSU5jn//POzyy675JhjjsnIkSPTv3//PPvssxk9enRWXXXVnHrqqWVH/FINHjw4v//97/Pwww9nk002ye67757u3bvnmWeeyejRo9O5c+ecfvrpZcdcYq677ro88sgjSZIXXnghSfLHP/4xDz74YJJPP0FmbrE+9thjc8cdd+Siiy7Ks88+m6997Wt55plnMmrUqAwYMCBDhw4t54so2MJ+T15++eV897vfzYwZM7L11lvn5ptvbnRfffr0yYEHHvjlhV9CmvM4ofroTI3pTCwsvWlerbk36UyN6UyN6Uyfr+oHTUmy7bbb5s4778yZZ56ZW265JbNmzco666yTX//61xkyZEjZ8b50b7zxRpJP32N/3nnnNXmdPn36KE2tXL9+/fKPf/wjZ5xxRu69996MGjUqvXv3zuGHH56f/OQn6dWrV9kRv1Rt27bNLbfckssuuyy33HJLbr755sycOTPLL7989ttvv5xwwgn5yle+UnbMJeaRRx6pnHtjrkcffTSPPvpo5d9znwy7du2akSNH5qyzzsptt92WBx54IL17984Pf/jDnHjiiencufOXmn1JWdjvybvvvpsZM2YkSf761782eV9bbbXVUlGamvM4oTrpTPPSmVhYetO8WnNv0pka05ka05k+X019fX1D2SEAAAAAaPla58dtAAAAAFA4gyYAAAAACmHQBAAAAEAhDJoAAAAAKIRBEwAAAACFMGgCAAAAoBAGTQAAAAAUwqAJAAAAgEIYNAEAAABQiHZlBwDKMXDgwDz00ENfeL1x48alrq5uyQf6jPr6+lx++eVJkp/97Gdf6toAAJ+lMwE0j0ETtHKrrLJKVllllQVub9fuy/81MWnSpJx99tlJlCYAoDroTAALx6AJWrkDDzxQMQEA+AI6E8DCcY4mAAAAAAph0AQstNGjR+eQQw7J2muvnV69eqVfv34ZMmRIRo4c2eT1Z86cmREjRuSoo47Klltumb59+6Z3795Zf/3184Mf/CDPP/98o9sMHTo0X/va1yr/rqurm+e/66+/Pkny+uuvVy5bkDPPPDN1dXUZOnToPJfPf9t77703++67b9ZYY4306NGjssZcI0aMyP7775/+/funV69e6d+/f77zne987vkabr311uyzzz5Zc801s9xyy2W11VbLxhtvnO9///u57bbbFng7AKDl05l0JmjNvHUO+EINDQ058cQTc+WVVyb5tMisvfbaeeeddzJq1KiMGjUqhx9+eM4999x5bvfKK6/k4IMPTps2bdKrV6/06dMns2bNyvjx4/PnP/85t9xyS6699trsvvvuldusueaa2WijjTJmzJgkyeabbz7PfS6//PKFfm2XXXZZTjrppNTV1WX11VdP165dK9tmzJiRww8/PCNGjEiSLLfccll77bUzfvz43HHHHfn73/+eU045JUcfffQ893naaaflvPPOq9xm3XXXzYwZM/LWW2/lr3/9a958883ssccehX4dAED5dCadCTBoAhbCb37zm1x55ZVZeeWVc/7552e33XarbLv33nvz//7f/8vvfve7bLzxxjnggAMq25Zbbrn89re/zS677JIePXpULp8xY0auu+66/PSnP82RRx6Zf/7zn+nSpUuS5IQTTsi+++5beYXuzjvvXKJf2y9/+cuceuqpOfLII9O2bdskybRp05IkJ510UkaMGJG11147F1544TwF7qabbspxxx2X//7v/85GG22UrbfeOknywQcf5MILL0y7du1y1VVXZa+99kpNTU3ldk8//XSeffbZJfo1AQDl0Jl0JsBb56DVO/vssxsdaj33v9tvvz319fU599xz07Zt2/zpT3+apzAlyU477ZTzzz8/SXLhhRfOs2355ZfP/vvvP09hSpKOHTvm8MMPzz777JOJEycu8WL0eb7zne/k6KOPrhSmJOncuXP+/e9/5w9/+EO6deuWP//5z41eJdxvv/1y0kknpaGhIRdffHHl8ldffTWzZ8/O2muvncGDB89TmJJkww03zMEHH7xkvygAoHA6k84ELBxHNEEr93kf1bvsssvm7rvvzuTJk7PJJptko402avJ6u+++e9q3b5+XXnop77zzTlZYYYV5tt9///25++6788orr+Tjjz/OnDlzkiQTJkxIkjz77LMZMmRIgV/VwltQgbn11lszZ86c7LzzzunTp0+T19lzzz1z8skn58EHH8zs2bPTtm3brLrqqkmSsWPHZsyYMQv8ngEALYvOpDMBC8egCVq5L/qo3v/+7/9O8unJIOd/Ze6z5r4K9eabb1ZK0+TJk3PQQQflH//4x+dm+PDDD5sbuzBf/epXm7x87kk3H3/88QV+3Q0NDUk+PWz8ww8/TK9evbLCCitk//33z5///OfsuOOO2XjjjbP11ltn0003zVZbbfW5J+IEAKqXzqQzAQvHoAn4XPX19UmS//u//8v//d//feH1p06dWvn/X/ziF/nHP/6Rnj175pe//GW22WabrLDCCuncuXOS5PTTT8+5556bWbNmLZHsC+OzJ7L8rLlf94QJEyqvIn6ez37dl156adZdd91ce+21efLJJ/Pkk08mSdq1a5fdd989p512WlZbbbXFDw8AVA2dSWcCPmXQBHyuuaXigAMOyBVXXLHQt/vkk0/yl7/8Jcmnn1Ky6667NrrOxIkTFznXZ9/H39DQ0Oh9/cm8Raa55n7dP/nJT3LSSSc167bt27fPMccck2OOOSZvvvlmHnvssTzwwAO55ZZbctttt+X555/PAw88kNra2kXOBwBUF51JZwI+5WTgwOdaZ511kiT//Oc/m3W7999/P5MnT06SbLnllk1e54knnmjy8qYK0Pw++6rae++91+R1XnnllS+8nwVZ1K97fiuvvHKGDBmSCy+8MA8//HC6deuW1157Lffee+9i3S8AUF10Jp0J+JRBE/C5dtttt3Tu3DnPPffcF5434LPmHuqdJO+++26j7ffff3+eeeaZJm8792N7kwW/wtazZ8/Ke/cff/zxRtvHjRuXUaNGLXTe+c399JO77747L7744iLfz2ettNJKlZNkvv3224XcJwBQHXQmnQn4lEET8Ll69eqVH/3oR0mSQw45JDfccEM++eSTea4zceLE3HDDDfnFL35Ruax79+5Zb731kiQ/+9nPKu/fT5IHHngg3//+99OpU6cm1+zZs2e6deuWJLnvvvsWmG3uCSdPO+20vP7665XLX3vttRx66KGVT2pZFOuuu24OPvjgzJo1K0OGDMmdd95ZOZHlXG+//XauuuqqeT6i+L777stPf/rTPP300/Ncf86cObnpppvyr3/9K0kyYMCARc4GAFQfnUlnAj7lHE3AFzr++OMzadKk/OY3v8nQoUPz4x//OGussUbatWuX9957LxMmTEhDQ0O22mqreW53yimn5Fvf+lbuueeerLvuulljjTUyadKkvP7661l//fWz/fbb55JLLmm0Xk1NTfbff//87ne/y3e/+9189atfTY8ePZIkw4YNy84775zk0zJ2991356WXXsomm2yS/v37Z86cOXnppZey3nrr5Ygjjsjw4cMX+es+99xzM23atNx000054IADUldXl379+iVJ3nnnncorbN/+9rcrt5k8eXKuuOKKXHHFFenWrVtWW221tG3bNhMmTMj777+fJDniiCPy9a9/fZFzAQDVSWfSmQBHNAELoaamJqecckpGjRqVAw88ML169cpLL72UZ599Np988kl22mmnnHPOObnyyivnud2OO+6Y2267Ldtvv31qamry73//Ox07dsyPfvSj3HXXXfMc7j2/U089NT/60Y+y5ppr5tVXX81DDz2Uhx56aJ5DyldbbbXcc8892WeffdKtW7e88sormTlzZo4//vjcddddi33iyA4dOuTKK6/M3/72twwZMiS1tbV54YUX8sILL6Rdu3YZOHBgLrnkkpx22mmV22y55ZY577zzsscee6RXr155/fXX8/zzz6ddu3bZbbfdcsMNN+Scc85ZrFwAQHXSmXQmIKmpr69v+OKrAQAAAMDnc0QTAAAAAIUwaAIAAACgEAZNAAAAABTCoAkAAACAQhg0AQAAAFAIgyYAAAAACmHQBAAAAEAhDJoAAAAAKIRBEwAAAACFMGgCAAAAoBAGTQAAAAAUwqAJAAAAgEIYNAEAAABQCIMmAAAAAArx/wMB/n8toaxzDQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x600 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_seq_patches_transp(embeddeds, add_cls=False, title='Image / Sequence')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 435
    },
    "id": "Vd2-zqL4NwUT",
    "outputId": "ae18e919-aefc-464f-82b6-e05ea150d489"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 22 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_seq_patches_transp(embeddeds, add_cls=True, title='Image / Sequence')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MekIClJxNwUU",
    "outputId": "a1f38349-7b70-4f3d-feed-274eca908f46"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameter containing:\n",
       "tensor([[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],\n",
       "       requires_grad=True)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls_token = nn.Parameter(torch.zeros(1, 1, 16))\n",
    "cls_token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xlwvhRoWNwUU",
    "outputId": "7243056c-4bb3-4793-b00a-0fadc3e7a3cf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([16, 1, 12, 12])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images, labels = next(iter(train_loader))\n",
    "images.shape # N, C, H, W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XryPjUspNwUU",
    "outputId": "d8f887b9-1cc5-4950-da77-13138d84d489"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([16, 9, 16])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embed = patch_embed(images)\n",
    "embed.shape # N, L, D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vRQ2w07VNwUU",
    "outputId": "37546a64-2991-4274-cea9-52c5bf027bb2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([16, 10, 16])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls_tokens = cls_token.expand(embed.size(0), -1, -1)\n",
    "embed_cls = torch.cat((cls_tokens, embed), dim=1)\n",
    "embed_cls.shape # N, L+1, D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XQ6MBraCNwUV"
   },
   "source": [
    "### The Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dR2z9hjpNwUV"
   },
   "source": [
    "![](https://github.com/dvgodoy/PyTorchStepByStep/blob/master/images/vit_model.png?raw=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "id": "Ri3i8FRzNwUV"
   },
   "outputs": [],
   "source": [
    "class ViT(nn.Module):\n",
    "    def __init__(self, encoder, img_size, in_channels, patch_size, n_outputs):\n",
    "        super().__init__()\n",
    "        self.d_model = encoder.d_model\n",
    "        self.n_outputs = n_outputs\n",
    "        self.encoder = encoder\n",
    "        self.mlp = nn.Linear(encoder.d_model, n_outputs)\n",
    "\n",
    "        self.embed = PatchEmbed(img_size, patch_size, in_channels, encoder.d_model)\n",
    "        self.cls_token = nn.Parameter(torch.zeros(1, 1, encoder.d_model))\n",
    "        \n",
    "    def preprocess(self, X):\n",
    "        # Patch embeddings\n",
    "        # N, C, H, W -> N, L, D\n",
    "        src = self.embed(X)\n",
    "        # Special classifier token\n",
    "        # 1, 1, D -> N, 1, D\n",
    "        cls_tokens = self.cls_token.expand(X.size(0), -1, -1)\n",
    "        # Concatenates CLS tokens -> N, 1 + L, D\n",
    "        src = torch.cat((cls_tokens, src), dim=1)\n",
    "        return src\n",
    "    \n",
    "    def encode(self, source):\n",
    "        # Encoder generates \"hidden states\"\n",
    "        states = self.encoder(source)\n",
    "        # Gets state from first token: CLS\n",
    "        cls_state = states[:, 0]  # N, 1, D\n",
    "        return cls_state\n",
    "        \n",
    "    def forward(self, X):\n",
    "        src = self.preprocess(X)\n",
    "        # Featurizer\n",
    "        cls_state = self.encode(src)\n",
    "        # Classifier\n",
    "        out = self.mlp(cls_state) # N, 1, outputs\n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RyplvskgNwUV"
   },
   "source": [
    "### Model Configuration & Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "id": "VdCzmTlxNwUW"
   },
   "outputs": [],
   "source": [
    "torch.manual_seed(17)\n",
    "layer = EncoderLayer(n_heads=2, d_model=16, ff_units=20)\n",
    "encoder = EncoderTransf(layer, n_layers=1)\n",
    "model_vit = ViT(encoder, img_size=12, in_channels=1, patch_size=4, n_outputs=3)\n",
    "multi_loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer_vit = optim.Adam(model_vit.parameters(), lr=1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "id": "v6iLsGP9NwUW"
   },
   "outputs": [],
   "source": [
    "sbs_vit = StepByStep(model_vit, multi_loss_fn, optimizer_vit)\n",
    "sbs_vit.set_loaders(train_loader, val_loader)\n",
    "sbs_vit.train(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "id": "DFVYbfTpNwUW",
    "outputId": "f3e5abcd-cb27-45a8-b86c-b0fafb19080e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sbs_vit.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hCdiBlVANwUW",
    "outputId": "857ee658-9908-40ce-8045-12b21869fe99"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameter containing:\n",
       "tensor([[[ 0.0590, -0.0471, -0.0053, -0.0251,  0.0360, -0.0394,  0.0585,\n",
       "          -0.0263,  0.0189, -0.0419,  0.0033, -0.0117,  0.0961, -0.0380,\n",
       "           0.0486, -0.0424]]], requires_grad=True)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_vit.cls_token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hdHnee0bNwUW",
    "outputId": "ff7e3f09-4ae9-425c-e5ef-c27b9d29626b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[76, 76],\n",
       "        [65, 65],\n",
       "        [59, 59]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "StepByStep.loader_apply(sbs_vit.val_loader, sbs_vit.correct)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-U-CDTv0NwUX"
   },
   "source": [
    "## Putting It All Together"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h05IC47VNwUX"
   },
   "source": [
    "### Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "id": "WoTuWHxQNwUX"
   },
   "outputs": [],
   "source": [
    "# Training set\n",
    "points, directions = generate_sequences(n=256, seed=13)\n",
    "full_train = torch.as_tensor(points).float()\n",
    "target_train = full_train[:, 2:]\n",
    "train_data = TensorDataset(full_train, target_train)\n",
    "generator = torch.Generator()\n",
    "train_loader = DataLoader(train_data, batch_size=16, shuffle=True, generator=generator)\n",
    "\n",
    "# Validation/Test Set\n",
    "test_points, test_directions = generate_sequences(seed=17)\n",
    "full_test = torch.as_tensor(test_points).float()\n",
    "source_test = full_test[:, :2]\n",
    "target_test = full_test[:, 2:]\n",
    "test_data = TensorDataset(source_test, target_test)\n",
    "test_loader = DataLoader(test_data, batch_size=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AO4kc1WxNwUe"
   },
   "source": [
    "### Model Assembly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3ae4wnjfNwUe"
   },
   "source": [
    "![](https://github.com/dvgodoy/PyTorchStepByStep/blob/master/images/full_transformer_and_class.png?raw=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mx-VKEb1NwUe"
   },
   "source": [
    "#### 1. Encoder-Decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "id": "daCuCKi8NwUf"
   },
   "outputs": [],
   "source": [
    "class EncoderDecoderTransf(nn.Module):\n",
    "    def __init__(self, encoder, decoder, input_len, target_len, n_features):\n",
    "        super(EncoderDecoderTransf, self).__init__()\n",
    "        self.encoder = encoder\n",
    "        self.decoder = decoder\n",
    "        self.input_len = input_len\n",
    "        self.target_len = target_len\n",
    "        self.trg_masks = self.subsequent_mask(self.target_len)\n",
    "        \n",
    "        self.n_features = n_features\n",
    "        self.proj = nn.Linear(n_features, encoder.d_model)\n",
    "        self.linear = nn.Linear(encoder.d_model, n_features)\n",
    "            \n",
    "    @staticmethod\n",
    "    def subsequent_mask(size):\n",
    "        attn_shape = (1, size, size)\n",
    "        subsequent_mask = (1 - torch.triu(torch.ones(attn_shape), diagonal=1))\n",
    "        return subsequent_mask\n",
    "\n",
    "    def encode(self, source_seq, source_mask=None):\n",
    "        # Projection\n",
    "        source_proj = self.proj(source_seq)\n",
    "        encoder_states = self.encoder(source_proj, source_mask)\n",
    "        self.decoder.init_keys(encoder_states)    \n",
    "        \n",
    "    def decode(self, shifted_target_seq, source_mask=None, target_mask=None):\n",
    "        # Projection\n",
    "        target_proj = self.proj(shifted_target_seq)\n",
    "        outputs = self.decoder(target_proj,\n",
    "                               source_mask=source_mask,\n",
    "                               target_mask=target_mask)\n",
    "        # Linear\n",
    "        outputs = self.linear(outputs)\n",
    "        return outputs\n",
    "    \n",
    "    def predict(self, source_seq, source_mask):\n",
    "        inputs = source_seq[:, -1:]\n",
    "        for i in range(self.target_len):\n",
    "            out = self.decode(inputs, source_mask, self.trg_masks[:, :i+1, :i+1])\n",
    "            out = torch.cat([inputs, out[:, -1:, :]], dim=-2)\n",
    "            inputs = out.detach()\n",
    "        outputs = inputs[:, 1:, :]\n",
    "        return outputs\n",
    "                \n",
    "    def forward(self, X, source_mask=None):\n",
    "        self.trg_masks = self.trg_masks.type_as(X).bool()\n",
    "        source_seq = X[:, :self.input_len, :]\n",
    "\n",
    "        self.encode(source_seq, source_mask)\n",
    "        if self.training:\n",
    "            shifted_target_seq = X[:, self.input_len-1:-1, :]\n",
    "            outputs = self.decode(shifted_target_seq, source_mask, self.trg_masks)\n",
    "        else:\n",
    "            outputs = self.predict(source_seq, source_mask)\n",
    "            \n",
    "        return outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pam5fHC7NwUg"
   },
   "source": [
    "#### 2. Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "id": "1_AaByiZNwUg"
   },
   "outputs": [],
   "source": [
    "class EncoderTransf(nn.Module):\n",
    "    def __init__(self, encoder_layer, n_layers=1, max_len=100):\n",
    "        super().__init__()\n",
    "        self.d_model = encoder_layer.d_model\n",
    "        self.pe = PositionalEncoding(max_len, self.d_model)\n",
    "        self.norm = nn.LayerNorm(self.d_model)\n",
    "        self.layers = nn.ModuleList([copy.deepcopy(encoder_layer)\n",
    "                                     for _ in range(n_layers)])\n",
    "    \n",
    "    def forward(self, query, mask=None):\n",
    "        # Positional Encoding\n",
    "        x = self.pe(query)\n",
    "        for layer in self.layers:\n",
    "            x = layer(x, mask)\n",
    "        # Norm\n",
    "        return self.norm(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H5-Rvc97NwUg"
   },
   "source": [
    "#### 3. Decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "id": "DllAVrJ7NwUh"
   },
   "outputs": [],
   "source": [
    "class DecoderTransf(nn.Module):\n",
    "    def __init__(self, decoder_layer, n_layers=1, max_len=100):\n",
    "        super(DecoderTransf, self).__init__()\n",
    "        self.d_model = decoder_layer.d_model\n",
    "        self.pe = PositionalEncoding(max_len, self.d_model)\n",
    "        self.norm = nn.LayerNorm(self.d_model)\n",
    "        self.layers = nn.ModuleList([copy.deepcopy(decoder_layer)\n",
    "                                     for _ in range(n_layers)])\n",
    "        \n",
    "    def init_keys(self, states):\n",
    "        for layer in self.layers:\n",
    "            layer.init_keys(states)\n",
    "    \n",
    "    def forward(self, query, source_mask=None, target_mask=None):\n",
    "        # Positional Encoding\n",
    "        x = self.pe(query)\n",
    "        for layer in self.layers:\n",
    "            x = layer(x, source_mask, target_mask)\n",
    "        # Norm\n",
    "        return self.norm(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-79xZjibNwUh"
   },
   "source": [
    "#### 4. Positional Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "id": "HIqo4_VSNwUh"
   },
   "outputs": [],
   "source": [
    "class PositionalEncoding(nn.Module):\n",
    "    def __init__(self, max_len, d_model):\n",
    "        super(PositionalEncoding, self).__init__()\n",
    "        self.d_model = d_model\n",
    "        pe = torch.zeros(max_len, d_model)\n",
    "        position = torch.arange(0, max_len).float().unsqueeze(1)\n",
    "        slope = torch.exp(torch.arange(0, d_model, 2).float() * (-np.log(10000.0) / d_model))\n",
    "        pe[:, 0::2] = torch.sin(position * slope) # even dimensions\n",
    "        pe[:, 1::2] = torch.cos(position * slope) # odd dimensions\n",
    "        self.register_buffer('pe', pe.unsqueeze(0))\n",
    "\n",
    "    def forward(self, x):\n",
    "        # x is N, L, D\n",
    "        # pe is 1, maxlen, D\n",
    "        scaled_x = x * np.sqrt(self.d_model)\n",
    "        encoded = scaled_x + self.pe[:, :x.size(1), :]\n",
    "        return encoded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "feWr1KpKNwUh"
   },
   "source": [
    "#### 5. Encoder \"Layer\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "id": "hVq71WuxNwUh"
   },
   "outputs": [],
   "source": [
    "class EncoderLayer(nn.Module):\n",
    "    def __init__(self, n_heads, d_model, ff_units, dropout=0.1):\n",
    "        super().__init__()\n",
    "        self.n_heads = n_heads\n",
    "        self.d_model = d_model\n",
    "        self.ff_units = ff_units\n",
    "        self.self_attn_heads = MultiHeadedAttention(n_heads, d_model, \n",
    "                                                    dropout=dropout)\n",
    "        self.ffn = nn.Sequential(\n",
    "            nn.Linear(d_model, ff_units),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(dropout),\n",
    "            nn.Linear(ff_units, d_model),\n",
    "        )\n",
    "        self.sublayers = nn.ModuleList([SubLayerWrapper(d_model, dropout) for _ in range(2)])\n",
    "         \n",
    "    def forward(self, query, mask=None):\n",
    "        # SubLayer 0 - Self-Attention\n",
    "        att = self.sublayers[0](query,\n",
    "                                sublayer=self.self_attn_heads, \n",
    "                                is_self_attn=True,\n",
    "                                mask=mask)\n",
    "        # SubLayer 1 - FFN\n",
    "        out = self.sublayers[1](att, sublayer=self.ffn)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DoNvE43BNwUh"
   },
   "source": [
    "#### 6. Decoder \"Layer\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "id": "aEFJ_P1ENwUi"
   },
   "outputs": [],
   "source": [
    "class DecoderLayer(nn.Module):\n",
    "    def __init__(self, n_heads, d_model, ff_units, dropout=0.1):\n",
    "        super().__init__()\n",
    "        self.n_heads = n_heads\n",
    "        self.d_model = d_model\n",
    "        self.ff_units = ff_units\n",
    "        self.self_attn_heads = MultiHeadedAttention(n_heads, d_model, \n",
    "                                                    dropout=dropout)\n",
    "        self.cross_attn_heads = MultiHeadedAttention(n_heads, d_model,\n",
    "                                                     dropout=dropout)\n",
    "        self.ffn = nn.Sequential(\n",
    "            nn.Linear(d_model, ff_units),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(dropout),\n",
    "            nn.Linear(ff_units, d_model),\n",
    "        )\n",
    "        self.sublayers = nn.ModuleList([SubLayerWrapper(d_model, dropout) for _ in range(3)])\n",
    "                \n",
    "    def init_keys(self, states):\n",
    "        self.cross_attn_heads.init_keys(states)\n",
    "         \n",
    "    def forward(self, query, source_mask=None, target_mask=None):\n",
    "        # SubLayer 0 - Masked Self-Attention\n",
    "        att1 = self.sublayers[0](query, \n",
    "                                 sublayer=self.self_attn_heads, \n",
    "                                 is_self_attn=True, \n",
    "                                 mask=target_mask)\n",
    "        # SubLayer 1 - Cross-Attention\n",
    "        att2 = self.sublayers[1](att1, \n",
    "                                 sublayer=self.cross_attn_heads, \n",
    "                                 mask=source_mask)\n",
    "        # SubLayer 2 - FFN\n",
    "        out = self.sublayers[2](att2, sublayer=self.ffn)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rvjwH1HDNwUi"
   },
   "source": [
    "#### 7. \"SubLayer\" Wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "id": "VKjDZ1FxNwUi"
   },
   "outputs": [],
   "source": [
    "class SubLayerWrapper(nn.Module):\n",
    "    def __init__(self, d_model, dropout):\n",
    "        super().__init__()\n",
    "        self.norm = nn.LayerNorm(d_model)\n",
    "        self.drop = nn.Dropout(dropout)\n",
    "        \n",
    "    def forward(self, x, sublayer, is_self_attn=False, **kwargs):\n",
    "        norm_x = self.norm(x)\n",
    "        if is_self_attn:\n",
    "            sublayer.init_keys(norm_x)\n",
    "        out = x + self.drop(sublayer(norm_x, **kwargs))\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "id": "MHDgTly6NwUj"
   },
   "outputs": [],
   "source": [
    "# Before\n",
    "def forward(self, query, mask=None):\n",
    "    # query and mask go in\n",
    "    norm_query = self.norm1(query)\n",
    "    self.self_attn_heads.init_keys(norm_query)\n",
    "    # the sublayer is the self-attention\n",
    "    states = self.self_attn_heads(norm_query, mask)\n",
    "    att = query + self.drop1(states)\n",
    "    # att comes out\n",
    "    ...\n",
    "    \n",
    "# After\n",
    "def forward(self, query, mask=None):\n",
    "    # query and mask go in\n",
    "    # the sublayer is the self-attention\n",
    "    # norm, drop, and residual are inside the wrapper\n",
    "    att = self.sublayers[0](query,\n",
    "                            sublayer=self.self_attn_heads, \n",
    "                            is_self_attn=True,\n",
    "                            mask=mask)\n",
    "    # att comes out\n",
    "    ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AZL83WGGNwUj"
   },
   "source": [
    "#### 8. Multi-Headed Attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "id": "9fdt8QohNwUj"
   },
   "outputs": [],
   "source": [
    "class MultiHeadedAttention(nn.Module):\n",
    "    def __init__(self, n_heads, d_model, dropout=0.1):\n",
    "        super(MultiHeadedAttention, self).__init__()\n",
    "        self.n_heads = n_heads\n",
    "        self.d_model = d_model\n",
    "        self.d_k = int(d_model / n_heads)\n",
    "        self.linear_query = nn.Linear(d_model, d_model)\n",
    "        self.linear_key = nn.Linear(d_model, d_model)\n",
    "        self.linear_value = nn.Linear(d_model, d_model)\n",
    "        self.linear_out = nn.Linear(d_model, d_model)\n",
    "        self.dropout = nn.Dropout(p=dropout)\n",
    "        self.alphas = None\n",
    "\n",
    "    def make_chunks(self, x):\n",
    "        batch_size, seq_len = x.size(0), x.size(1)\n",
    "        # N, L, D -> N, L, n_heads * d_k\n",
    "        x = x.view(batch_size, seq_len, self.n_heads, self.d_k) \n",
    "        # N, n_heads, L, d_k\n",
    "        x = x.transpose(1, 2)  \n",
    "        return x\n",
    "\n",
    "    def init_keys(self, key):\n",
    "        # N, n_heads, L, d_k\n",
    "        self.proj_key = self.make_chunks(self.linear_key(key))\n",
    "        self.proj_value = self.make_chunks(self.linear_value(key))\n",
    "        \n",
    "    def score_function(self, query):\n",
    "        # scaled dot product\n",
    "        # N, n_heads, L, d_k x # N, n_heads, d_k, L -> N, n_heads, L, L\n",
    "        proj_query = self.make_chunks(self.linear_query(query))\n",
    "        dot_products = torch.matmul(proj_query, \n",
    "                                    self.proj_key.transpose(-2, -1))\n",
    "        scores =  dot_products / np.sqrt(self.d_k)\n",
    "        return scores\n",
    "            \n",
    "    def attn(self, query, mask=None):\n",
    "        # Query is batch-first: N, L, D\n",
    "        # Score function will generate scores for each head\n",
    "        scores = self.score_function(query) # N, n_heads, L, L\n",
    "        if mask is not None:\n",
    "            scores = scores.masked_fill(mask == 0, -1e9)\n",
    "        alphas = F.softmax(scores, dim=-1) # N, n_heads, L, L\n",
    "        alphas = self.dropout(alphas)\n",
    "        self.alphas = alphas.detach()\n",
    "        \n",
    "        # N, n_heads, L, L x N, n_heads, L, d_k -> N, n_heads, L, d_k\n",
    "        context = torch.matmul(alphas, self.proj_value)\n",
    "        return context        \n",
    "                                \n",
    "    def output_function(self, contexts):\n",
    "        # N, L, D\n",
    "        out = self.linear_out(contexts) # N, L, D\n",
    "        return out\n",
    "        \n",
    "    def forward(self, query, mask=None):\n",
    "        if mask is not None:\n",
    "            # N, 1, L, L - every head uses the same mask\n",
    "            mask = mask.unsqueeze(1)\n",
    "\n",
    "        # N, n_heads, L, d_k\n",
    "        context = self.attn(query, mask=mask)\n",
    "        # N, L, n_heads, d_k\n",
    "        context = context.transpose(1, 2).contiguous()\n",
    "        # N, L, n_heads * d_k = N, L, d_model\n",
    "        context = context.view(query.size(0), -1, self.d_model)        \n",
    "        # N, L, d_model\n",
    "        out = self.output_function(context)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D0EbQOBmNwUj"
   },
   "source": [
    "### Model Configuration & Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "id": "relmLO6nNwUk"
   },
   "outputs": [],
   "source": [
    "torch.manual_seed(42)\n",
    "# Layers\n",
    "enclayer = EncoderLayer(n_heads=3, d_model=6, ff_units=10, dropout=0.1)\n",
    "declayer = DecoderLayer(n_heads=3, d_model=6, ff_units=10, dropout=0.1)\n",
    "# Encoder and Decoder\n",
    "enctransf = EncoderTransf(enclayer, n_layers=2)\n",
    "dectransf = DecoderTransf(declayer, n_layers=2)\n",
    "# Transformer\n",
    "model_transf = EncoderDecoderTransf(enctransf, dectransf, input_len=2, target_len=2, n_features=2)\n",
    "loss = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(model_transf.parameters(), lr=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "id": "WNWKnLgANwUk"
   },
   "outputs": [],
   "source": [
    "for p in model_transf.parameters():\n",
    "    if p.dim() > 1:\n",
    "        nn.init.xavier_uniform_(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "id": "5lcICEImNwUk"
   },
   "outputs": [],
   "source": [
    "sbs_seq_transf = StepByStep(model_transf, loss, optimizer)\n",
    "sbs_seq_transf.set_loaders(train_loader, test_loader)\n",
    "sbs_seq_transf.train(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NcXuFB_INwUk",
    "outputId": "34ef2a00-9651-4792-d05a-39a65a07b3ef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.05839654209557921, 0.020480516366660595)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sbs_seq_transf.losses[-1], sbs_seq_transf.val_losses[-1]"
   ]
  }
 ],
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  "accelerator": "GPU",
  "colab": {
   "name": "Chapter10.ipynb",
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  "kernelspec": {
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   "language": "python",
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